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Wiener or Brownian motion process on the surface of a sphere. The Wiener process is widely considered the most studied and central stochastic process in probability theory. Stochastic processes are widely used as mathematical models of systems and phenomena that appear to vary in a random manner. They have applications in many disciplines such as biology, chemistry, ecology, neuroscience, physics, image processing, signal processing, control theory,  information theory, computer science, cryptography and telecommunications. Furthermore, seemingly random changes in financial markets have motivated the extensive use of stochastic processes in finance.

Wiener or Brownian motion process on the surface of a sphere. The Wiener process is widely considered the most studied and central stochastic process in probability theory. Stochastic processes are widely used as mathematical models of systems and phenomena that appear to vary in a random manner. They have applications in many disciplines such as biology, chemistry, ecology, neuroscience, physics, image processing, signal processing, control theory,  information theory, computer science, cryptography and telecommunications. Furthermore, seemingly random changes in financial markets have motivated the extensive use of stochastic processes in finance.

球面上的 Wiener 或 Brownian 运动过程。维纳过程被广泛认为是随机过程概率论研究最多和最核心的维纳过程。'''<font color="#ff8000"> 随机过程Stochastic processes</font>'''被广泛用作以随机方式变化的系统和现象的数学模型。它们在生物学、化学、生态学、神经科学、物理学、图像处理、信号处理、控制理论、信息理论、计算机科学、密码学和电信学等许多学科都有应用。此外，金融市场表面上的随机变化促使'''<font color="#ff8000"> 随机过程Stochastic processes</font>'''在金融领域的广泛应用。

球面上的 Wiener 或 Brownian 运动过程。'''<font color="#ff8000"> 维纳过程Wiener process</font>'''被广泛认为是概率论研究最多和最核心的'''<font color="#ff8000"> 随机过程Stochastic processes</font>'''。'''<font color="#ff8000"> 随机过程Stochastic processes</font>'''被广泛用作以随机方式变化的系统和现象的数学模型。它们在生物学、化学、生态学、神经科学、物理学、图像处理、信号处理、控制理论、信息理论、计算机科学、密码学和电信学等许多学科都有应用。此外，金融市场表面上的随机变化促进了'''<font color="#ff8000"> 随机过程Stochastic processes</font>'''在金融领域的广泛应用。

In [[probability theory]] and related fields, a '''stochastic''' or '''random process''' is a [[mathematical object]] usually defined as a [[Indexed family|family]] of [[random variable]]s. Historically, the random variables were associated with or indexed by a set of numbers, usually viewed as points in time, giving the interpretation of a stochastic process representing numerical values of some system [[random]]ly changing over [[time]], such as the growth of a [[bacteria]]l population, an [[electrical current]] fluctuating due to [[thermal noise]], or the movement of a [[gas]] [[molecule]].<ref name="doob1953stochasticP46to47">{{cite book|author=Joseph L. Doob|title=Stochastic processes|url=https://books.google.com/books?id=7Bu8jgEACAAJ|year=1990|publisher=Wiley|pages=46, 47}}</ref><ref name="Parzen1999">{{cite book|author=Emanuel Parzen|title=Stochastic Processes|url=https://books.google.com/books?id=0mB2CQAAQBAJ|year= 2015|publisher=Courier Dover Publications|isbn=978-0-486-79688-8|pages=7, 8}}</ref><ref name="GikhmanSkorokhod1969page1">{{cite book|author1=Iosif Ilyich Gikhman|author2=Anatoly Vladimirovich Skorokhod|title=Introduction to the Theory of Random Processes|url=https://books.google.com/books?id=q0lo91imeD0C|year=1969|publisher=Courier Corporation|isbn=978-0-486-69387-3|page=1}}</ref><ref name=":0">{{Cite book|title=Markov Chains: From Theory to Implementation and Experimentation|last=Gagniuc|first=Paul A.|publisher=John Wiley & Sons|year=2017|isbn=978-1-119-38755-8|location= NJ|pages=1–235}}</ref> Stochastic processes are widely used as [[mathematical models]] of systems and phenomena that appear to vary in a random manner. They have applications in many disciplines such as [[biology]],<ref name="Bressloff2014">{{cite book|author=Paul C. Bressloff|title=Stochastic Processes in Cell Biology|url=https://books.google.com/books?id=SwZYBAAAQBAJ|year=2014|publisher=Springer|isbn=978-3-319-08488-6}}</ref> [[chemistry]],<ref name="Kampen2011">{{cite book|author=N.G. Van Kampen|title=Stochastic Processes in Physics and Chemistry|url=https://books.google.com/books?id=N6II-6HlPxEC|year=2011|publisher=Elsevier|isbn=978-0-08-047536-3}}</ref> [[ecology]],<ref name="LandeEngen2003">{{cite book|author1=Russell Lande|author2=Steinar Engen|author3=Bernt-Erik Sæther|title=Stochastic Population Dynamics in Ecology and Conservation|url=https://books.google.com/books?id=6KClauq8OekC|year=2003|publisher=Oxford University Press|isbn=978-0-19-852525-7}}</ref> [[neuroscience]]<ref name="LaingLord2010">{{cite book|author1=Carlo Laing|author2=Gabriel J Lord|title=Stochastic Methods in Neuroscience|url=https://books.google.com/books?id=RaYSDAAAQBAJ|year=2010|publisher=OUP Oxford|isbn=978-0-19-923507-0}}</ref>, [[physics]]<ref name="PaulBaschnagel2013">{{cite book|author1=Wolfgang Paul|author2=Jörg Baschnagel|title=Stochastic Processes: From Physics to Finance|url=https://books.google.com/books?id=OWANAAAAQBAJ|year=2013|publisher=Springer Science & Business Media|isbn=978-3-319-00327-6}}</ref>, [[image processing]], [[signal processing]],<ref name="Dougherty1999">{{cite book|author=Edward R. Dougherty|title=Random processes for image and signal processing|url=https://books.google.com/books?id=ePxDAQAAIAAJ|year=1999|publisher=SPIE Optical Engineering Press|isbn=978-0-8194-2513-3}}</ref> [[Stochastic control|control theory]], <ref name="Bertsekas1996">{{cite book|author=Dimitri P. Bertsekas|title=Stochastic Optimal Control: The Discrete-Time Case|url=http://www.athenasc.com/socbook.html|year=1996|publisher=Athena Scientific]|isbn=1-886529-03-5}}</ref>  [[information theory]],<ref name="CoverThomas2012page71">{{cite book|author1=Thomas M. Cover|author2=Joy A. Thomas|title=Elements of Information Theory|url=https://books.google.com/books?id=VWq5GG6ycxMC=PT16|year=2012|publisher=John Wiley & Sons|isbn=978-1-118-58577-1|page=71}}</ref> [[computer science]],<ref name="Baron2015">{{cite book|author=Michael Baron|title=Probability and Statistics for Computer Scientists, Second Edition|url=https://books.google.com/books?id=CwQZCwAAQBAJ|year=2015|publisher=CRC Press|isbn=978-1-4987-6060-7|page=131}}</ref> [[cryptography]]<ref>{{cite book|author1=Jonathan Katz|author2=Yehuda Lindell|title=Introduction to Modern Cryptography: Principles and Protocols|url=https://archive.org/details/Introduction_to_Modern_Cryptography|year=2007|publisher=CRC Press|isbn=978-1-58488-586-3|page=[https://archive.org/details/Introduction_to_Modern_Cryptography/page/n44 26]}}</ref> and [[telecommunications]].<ref name="BaccelliBlaszczyszyn2009">{{cite book|author1=François Baccelli|author2=Bartlomiej Blaszczyszyn|title=Stochastic Geometry and Wireless Networks|url=https://books.google.com/books?id=H3ZkTN2pYS4C|year=2009|publisher=Now Publishers Inc|isbn=978-1-60198-264-3}}</ref> Furthermore, seemingly random changes in [[financial markets]] have motivated the extensive use of stochastic processes in [[finance]].<ref name="Steele2001">{{cite book|author=J. Michael Steele|title=Stochastic Calculus and Financial Applications|url=https://books.google.com/books?id=H06xzeRQgV4C|year=2001|publisher=Springer Science & Business Media|isbn=978-0-387-95016-7}}</ref><ref name="MusielaRutkowski2006">{{cite book|author1=Marek Musiela|author2=Marek Rutkowski|title=Martingale Methods in Financial Modelling|url=https://books.google.com/books?id=iojEts9YAxIC|year= 2006|publisher=Springer Science & Business Media|isbn=978-3-540-26653-2}}</ref><ref name="Shreve2004">{{cite book|author=Steven E. Shreve|title=Stochastic Calculus for Finance II: Continuous-Time Models|url=https://books.google.com/books?id=O8kD1NwQBsQC|year=2004|publisher=Springer Science & Business Media|isbn=978-0-387-40101-0}}</ref>

In [[probability theory]] and related fields, a '''stochastic''' or '''random process''' is a [[mathematical object]] usually defined as a [[Indexed family|family]] of [[random variable]]s. Historically, the random variables were associated with or indexed by a set of numbers, usually viewed as points in time, giving the interpretation of a stochastic process representing numerical values of some system [[random]]ly changing over [[time]], such as the growth of a [[bacteria]]l population, an [[electrical current]] fluctuating due to [[thermal noise]], or the movement of a [[gas]] [[molecule]].<ref name="doob1953stochasticP46to47">{{cite book|author=Joseph L. Doob|title=Stochastic processes|url=https://books.google.com/books?id=7Bu8jgEACAAJ|year=1990|publisher=Wiley|pages=46, 47}}</ref><ref name="Parzen1999">{{cite book|author=Emanuel Parzen|title=Stochastic Processes|url=https://books.google.com/books?id=0mB2CQAAQBAJ|year= 2015|publisher=Courier Dover Publications|isbn=978-0-486-79688-8|pages=7, 8}}</ref><ref name="GikhmanSkorokhod1969page1">{{cite book|author1=Iosif Ilyich Gikhman|author2=Anatoly Vladimirovich Skorokhod|title=Introduction to the Theory of Random Processes|url=https://books.google.com/books?id=q0lo91imeD0C|year=1969|publisher=Courier Corporation|isbn=978-0-486-69387-3|page=1}}</ref><ref name=":0">{{Cite book|title=Markov Chains: From Theory to Implementation and Experimentation|last=Gagniuc|first=Paul A.|publisher=John Wiley & Sons|year=2017|isbn=978-1-119-38755-8|location= NJ|pages=1–235}}</ref> Stochastic processes are widely used as [[mathematical models]] of systems and phenomena that appear to vary in a random manner. They have applications in many disciplines such as [[biology]],<ref name="Bressloff2014">{{cite book|author=Paul C. Bressloff|title=Stochastic Processes in Cell Biology|url=https://books.google.com/books?id=SwZYBAAAQBAJ|year=2014|publisher=Springer|isbn=978-3-319-08488-6}}</ref> [[chemistry]],<ref name="Kampen2011">{{cite book|author=N.G. Van Kampen|title=Stochastic Processes in Physics and Chemistry|url=https://books.google.com/books?id=N6II-6HlPxEC|year=2011|publisher=Elsevier|isbn=978-0-08-047536-3}}</ref> [[ecology]],<ref name="LandeEngen2003">{{cite book|author1=Russell Lande|author2=Steinar Engen|author3=Bernt-Erik Sæther|title=Stochastic Population Dynamics in Ecology and Conservation|url=https://books.google.com/books?id=6KClauq8OekC|year=2003|publisher=Oxford University Press|isbn=978-0-19-852525-7}}</ref> [[neuroscience]]<ref name="LaingLord2010">{{cite book|author1=Carlo Laing|author2=Gabriel J Lord|title=Stochastic Methods in Neuroscience|url=https://books.google.com/books?id=RaYSDAAAQBAJ|year=2010|publisher=OUP Oxford|isbn=978-0-19-923507-0}}</ref>, [[physics]]<ref name="PaulBaschnagel2013">{{cite book|author1=Wolfgang Paul|author2=Jörg Baschnagel|title=Stochastic Processes: From Physics to Finance|url=https://books.google.com/books?id=OWANAAAAQBAJ|year=2013|publisher=Springer Science & Business Media|isbn=978-3-319-00327-6}}</ref>, [[image processing]], [[signal processing]],<ref name="Dougherty1999">{{cite book|author=Edward R. Dougherty|title=Random processes for image and signal processing|url=https://books.google.com/books?id=ePxDAQAAIAAJ|year=1999|publisher=SPIE Optical Engineering Press|isbn=978-0-8194-2513-3}}</ref> [[Stochastic control|control theory]], <ref name="Bertsekas1996">{{cite book|author=Dimitri P. Bertsekas|title=Stochastic Optimal Control: The Discrete-Time Case|url=http://www.athenasc.com/socbook.html|year=1996|publisher=Athena Scientific]|isbn=1-886529-03-5}}</ref>  [[information theory]],<ref name="CoverThomas2012page71">{{cite book|author1=Thomas M. Cover|author2=Joy A. Thomas|title=Elements of Information Theory|url=https://books.google.com/books?id=VWq5GG6ycxMC=PT16|year=2012|publisher=John Wiley & Sons|isbn=978-1-118-58577-1|page=71}}</ref> [[computer science]],<ref name="Baron2015">{{cite book|author=Michael Baron|title=Probability and Statistics for Computer Scientists, Second Edition|url=https://books.google.com/books?id=CwQZCwAAQBAJ|year=2015|publisher=CRC Press|isbn=978-1-4987-6060-7|page=131}}</ref> [[cryptography]]<ref>{{cite book|author1=Jonathan Katz|author2=Yehuda Lindell|title=Introduction to Modern Cryptography: Principles and Protocols|url=https://archive.org/details/Introduction_to_Modern_Cryptography|year=2007|publisher=CRC Press|isbn=978-1-58488-586-3|page=[https://archive.org/details/Introduction_to_Modern_Cryptography/page/n44 26]}}</ref> and [[telecommunications]].<ref name="BaccelliBlaszczyszyn2009">{{cite book|author1=François Baccelli|author2=Bartlomiej Blaszczyszyn|title=Stochastic Geometry and Wireless Networks|url=https://books.google.com/books?id=H3ZkTN2pYS4C|year=2009|publisher=Now Publishers Inc|isbn=978-1-60198-264-3}}</ref> Furthermore, seemingly random changes in [[financial markets]] have motivated the extensive use of stochastic processes in [[finance]].<ref name="Steele2001">{{cite book|author=J. Michael Steele|title=Stochastic Calculus and Financial Applications|url=https://books.google.com/books?id=H06xzeRQgV4C|year=2001|publisher=Springer Science & Business Media|isbn=978-0-387-95016-7}}</ref><ref name="MusielaRutkowski2006">{{cite book|author1=Marek Musiela|author2=Marek Rutkowski|title=Martingale Methods in Financial Modelling|url=https://books.google.com/books?id=iojEts9YAxIC|year= 2006|publisher=Springer Science & Business Media|isbn=978-3-540-26653-2}}</ref><ref name="Shreve2004">{{cite book|author=Steven E. Shreve|title=Stochastic Calculus for Finance II: Continuous-Time Models|url=https://books.google.com/books?id=O8kD1NwQBsQC|year=2004|publisher=Springer Science & Business Media|isbn=978-0-387-40101-0}}</ref>

 

Applications and the study of phenomena have in turn inspired the proposal of new stochastic processes. Examples of such stochastic processes include the Wiener process or Brownian motion process, used by Louis Bachelier to study price changes on the Paris Bourse, and the Poisson process, used by A. K. Erlang to study the number of phone calls occurring in a certain period of time. These two stochastic processes are considered the most important and central in the theory of stochastic processes, and were discovered repeatedly and independently, both before and after Bachelier and Erlang, in different settings and countries.

Applications and the study of phenomena have in turn inspired the proposal of new stochastic processes. Examples of such stochastic processes include the Wiener process or Brownian motion process, used by Louis Bachelier to study price changes on the Paris Bourse, and the Poisson process, used by A. K. Erlang to study the number of phone calls occurring in a certain period of time. These two stochastic processes are considered the most important and central in the theory of stochastic processes, and were discovered repeatedly and independently, both before and after Bachelier and Erlang, in different settings and countries.

现象的应用和研究反过来激发了新的随机过程的提出。这类随机过程的例子包括路易斯 · 巴舍利耶用来研究巴黎证券交易所价格变化的维纳过程或布朗运动过程，以及 a · k · 埃尔朗用来研究在一定时期内通话次数的泊松过程。这两个随机过程在随机过程理论中被认为是最重要和最核心的，并且在巴舍利耶和 Erlang 之前和之后，在不同的环境和国家被重复和独立地发现。

现象的应用和研究反过来激发了新的随机过程的提出。这类随机过程的例子包括路易斯 · 巴舍利耶用来研究巴黎证券交易所价格变化的'''<font color="#ff8000"> 维纳过程Wiener process</font>''或'''<font color="#ff8000"> 布朗运动过程Brownian motion process</font>''，以及 a · k · 埃尔朗用来研究在一定时期内通话次数的'''<font color="#ff8000"> 泊松过程Poisson process</font>'''。这两个随机过程在随机过程理论中被认为是最重要和最核心的，并且在巴舍利耶和 Erlang 之前和之后，在不同的环境和国家被重复和独立地发现。

Applications and the study of phenomena have in turn inspired the proposal of new stochastic processes. Examples of such stochastic processes include the [[Wiener process]] or Brownian motion process,{{efn|The term ''Brownian motion'' can refer to the physical process, also known as ''Brownian movement'', and the stochastic process, a mathematical object, but to avoid ambiguity this article uses the terms ''Brownian motion process'' or ''Wiener process'' for the latter in a style similar to, for example, Gikhman and Skorokhod<ref name="GikhmanSkorokhod1969">{{cite book|author1=Iosif Ilyich Gikhman|author2=Anatoly Vladimirovich Skorokhod|title=Introduction to the Theory of Random Processes|url=https://books.google.com/books?id=yJyLzG7N7r8C|year=1969|publisher=Courier Corporation|isbn=978-0-486-69387-3}}</ref> or Rosenblatt.<ref name="Rosenblatt1962">{{cite book|author=Murray Rosenblatt|title=Random Processes|url=https://archive.org/details/randomprocesses00rose_0|url-access=registration|year=1962|publisher=Oxford University Press}}</ref>}} used by [[Louis Bachelier]] to study price changes on the [[Paris Bourse]],<ref name="JarrowProtter2004">{{cite book|last1=Jarrow|first1=Robert|title=A Festschrift for Herman Rubin|last2=Protter|first2=Philip|chapter=A short history of stochastic integration and mathematical finance: the early years, 1880–1970|year=2004|pages=75–80|issn=0749-2170|doi=10.1214/lnms/1196285381|citeseerx=10.1.1.114.632|series=Institute of Mathematical Statistics Lecture Notes - Monograph Series|isbn=978-0-940600-61-4}}</ref> and the [[Poisson process]], used by [[A. K. Erlang]] to study the number of phone calls occurring in a certain period of time.<ref name="Stirzaker2000">{{cite journal|last1=Stirzaker|first1=David|title=Advice to Hedgehogs, or, Constants Can Vary|journal=The Mathematical Gazette|volume=84|issue=500|year=2000|pages=197–210|issn=0025-5572|doi=10.2307/3621649|jstor=3621649}}</ref> These two stochastic processes are considered the most important and central in the theory of stochastic processes,<ref name="doob1953stochasticP46to47"/><ref name="Parzen1999"/><ref>{{cite book|author1=Donald L. Snyder|author2=Michael I. Miller|title=Random Point Processes in Time and Space|url=https://books.google.com/books?id=c_3UBwAAQBAJ|year=2012|publisher=Springer Science & Business Media|isbn=978-1-4612-3166-0|page=32}}</ref> and were discovered repeatedly and independently, both before and after Bachelier and Erlang, in different settings and countries.<ref name="JarrowProtter2004"/><ref name="GuttorpThorarinsdottir2012">{{cite journal|last1=Guttorp|first1=Peter|last2=Thorarinsdottir|first2=Thordis L.|title=What Happened to Discrete Chaos, the Quenouille Process, and the Sharp Markov Property? Some History of Stochastic Point Processes|journal=International Statistical Review|volume=80|issue=2|year=2012|pages=253–268|issn=0306-7734|doi=10.1111/j.1751-5823.2012.00181.x}}</ref>

Applications and the study of phenomena have in turn inspired the proposal of new stochastic processes. Examples of such stochastic processes include the [[Wiener process]] or Brownian motion process,{{efn|The term ''Brownian motion'' can refer to the physical process, also known as ''Brownian movement'', and the stochastic process, a mathematical object, but to avoid ambiguity this article uses the terms ''Brownian motion process'' or ''Wiener process'' for the latter in a style similar to, for example, Gikhman and Skorokhod<ref name="GikhmanSkorokhod1969">{{cite book|author1=Iosif Ilyich Gikhman|author2=Anatoly Vladimirovich Skorokhod|title=Introduction to the Theory of Random Processes|url=https://books.google.com/books?id=yJyLzG7N7r8C|year=1969|publisher=Courier Corporation|isbn=978-0-486-69387-3}}</ref> or Rosenblatt.<ref name="Rosenblatt1962">{{cite book|author=Murray Rosenblatt|title=Random Processes|url=https://archive.org/details/randomprocesses00rose_0|url-access=registration|year=1962|publisher=Oxford University Press}}</ref>}} used by [[Louis Bachelier]] to study price changes on the [[Paris Bourse]],<ref name="JarrowProtter2004">{{cite book|last1=Jarrow|first1=Robert|title=A Festschrift for Herman Rubin|last2=Protter|first2=Philip|chapter=A short history of stochastic integration and mathematical finance: the early years, 1880–1970|year=2004|pages=75–80|issn=0749-2170|doi=10.1214/lnms/1196285381|citeseerx=10.1.1.114.632|series=Institute of Mathematical Statistics Lecture Notes - Monograph Series|isbn=978-0-940600-61-4}}</ref> and the [[Poisson process]], used by [[A. K. Erlang]] to study the number of phone calls occurring in a certain period of time.<ref name="Stirzaker2000">{{cite journal|last1=Stirzaker|first1=David|title=Advice to Hedgehogs, or, Constants Can Vary|journal=The Mathematical Gazette|volume=84|issue=500|year=2000|pages=197–210|issn=0025-5572|doi=10.2307/3621649|jstor=3621649}}</ref> These two stochastic processes are considered the most important and central in the theory of stochastic processes,<ref name="doob1953stochasticP46to47"/><ref name="Parzen1999"/><ref>{{cite book|author1=Donald L. Snyder|author2=Michael I. Miller|title=Random Point Processes in Time and Space|url=https://books.google.com/books?id=c_3UBwAAQBAJ|year=2012|publisher=Springer Science & Business Media|isbn=978-1-4612-3166-0|page=32}}</ref> and were discovered repeatedly and independently, both before and after Bachelier and Erlang, in different settings and countries.<ref name="JarrowProtter2004"/><ref name="GuttorpThorarinsdottir2012">{{cite journal|last1=Guttorp|first1=Peter|last2=Thorarinsdottir|first2=Thordis L.|title=What Happened to Discrete Chaos, the Quenouille Process, and the Sharp Markov Property? Some History of Stochastic Point Processes|journal=International Statistical Review|volume=80|issue=2|year=2012|pages=253–268|issn=0306-7734|doi=10.1111/j.1751-5823.2012.00181.x}}</ref>

 

The term random function is also used to refer to a stochastic or random process, because a stochastic process can also be interpreted as a random element in a function space. The terms stochastic process and random process are used interchangeably, often with no specific mathematical space for the set that indexes the random variables. But often these two terms are used when the random variables are indexed by the integers or an interval of the real line. The values of a stochastic process are not always numbers and can be vectors or other mathematical objects. martingales, Markov processes, Lévy processes, Gaussian processes, random fields, renewal processes, and branching processes. The study of stochastic processes uses mathematical knowledge and techniques from probability, calculus, linear algebra, set theory, and topology as well as branches of mathematical analysis such as real analysis, measure theory, Fourier analysis, and functional analysis. The theory of stochastic processes is considered to be an important contribution to mathematics and it continues to be an active topic of research for both theoretical reasons and applications.

The term random function is also used to refer to a stochastic or random process, because a stochastic process can also be interpreted as a random element in a function space. The terms stochastic process and random process are used interchangeably, often with no specific mathematical space for the set that indexes the random variables. But often these two terms are used when the random variables are indexed by the integers or an interval of the real line. The values of a stochastic process are not always numbers and can be vectors or other mathematical objects. martingales, Markov processes, Lévy processes, Gaussian processes, random fields, renewal processes, and branching processes. The study of stochastic processes uses mathematical knowledge and techniques from probability, calculus, linear algebra, set theory, and topology as well as branches of mathematical analysis such as real analysis, measure theory, Fourier analysis, and functional analysis. The theory of stochastic processes is considered to be an important contribution to mathematics and it continues to be an active topic of research for both theoretical reasons and applications.

The term '''random function''' is also used to refer to a stochastic or random process,<ref name="GusakKukush2010page21">{{cite book|first1=Dmytro|last1=Gusak|first2=Alexander|last2=Kukush|first3=Alexey|last3=Kulik|first4=Yuliya|last4=Mishura|author4-link=Yuliya Mishura|first5=Andrey|last5=Pilipenko|title=Theory of Stochastic Processes: With Applications to Financial Mathematics and Risk Theory|url=https://books.google.com/books?id=8Nzn51YTbX4C|year=2010|publisher=Springer Science & Business Media|isbn=978-0-387-87862-1|page=21|ref=harv}}</ref><ref name="Skorokhod2005page42">{{cite book|author=Valeriy Skorokhod|title=Basic Principles and Applications of Probability Theory|url=https://books.google.com/books?id=dQkYMjRK3fYC|year= 2005|publisher=Springer Science & Business Media|isbn=978-3-540-26312-8|page=42}}</ref> because a stochastic process can also be interpreted as a random element in a [[function space]].<ref name="Kallenberg2002page24"/><ref name="Lamperti1977page1">{{cite book|author=John Lamperti|title=Stochastic processes: a survey of the mathematical theory|url=https://books.google.com/books?id=Pd4cvgAACAAJ|year=1977|publisher=Springer-Verlag|isbn=978-3-540-90275-1|pages=1–2}}</ref> The terms ''stochastic process'' and ''random process'' are used interchangeably, often with no specific [[mathematical space]] for the set that indexes the random variables.<ref name="Kallenberg2002page24">{{cite book|author=Olav Kallenberg|title=Foundations of Modern Probability|url=https://books.google.com/books?id=L6fhXh13OyMC|year=2002|publisher=Springer Science & Business Media|isbn=978-0-387-95313-7|pages=24–25}}</ref><ref name="ChaumontYor2012">{{cite book|author1=Loïc Chaumont|author2=Marc Yor|title=Exercises in Probability: A Guided Tour from Measure Theory to Random Processes, Via Conditioning|url=https://books.google.com/books?id=1dcqV9mtQloC&pg=PR4|year= 2012|publisher=Cambridge University Press|isbn=978-1-107-60655-5|page=175}}</ref> But often these two terms are used when the random variables are indexed by the [[integers]] or an [[Interval (mathematics)|interval]] of the [[real line]].<ref name="GikhmanSkorokhod1969page1"/><ref name="ChaumontYor2012"/> If the random variables are indexed by the [[Cartesian plane]] or some higher-dimensional [[Euclidean space]], then the collection of random variables is usually called a [[random field]] instead.<ref name="GikhmanSkorokhod1969page1"/><ref name="AdlerTaylor2009page7">{{cite book|author1=Robert J. Adler|author2=Jonathan E. Taylor|title=Random Fields and Geometry|url=https://books.google.com/books?id=R5BGvQ3ejloC|year=2009|publisher=Springer Science & Business Media|isbn=978-0-387-48116-6|pages=7–8}}</ref> The values of a stochastic process are not always numbers and can be vectors or other mathematical objects.<ref name="GikhmanSkorokhod1969page1"/><ref name="Lamperti1977page1"/>

The term '''random function''' is also used to refer to a stochastic or random process,<ref name="GusakKukush2010page21">{{cite book|first1=Dmytro|last1=Gusak|first2=Alexander|last2=Kukush|first3=Alexey|last3=Kulik|first4=Yuliya|last4=Mishura|author4-link=Yuliya Mishura|first5=Andrey|last5=Pilipenko|title=Theory of Stochastic Processes: With Applications to Financial Mathematics and Risk Theory|url=https://books.google.com/books?id=8Nzn51YTbX4C|year=2010|publisher=Springer Science & Business Media|isbn=978-0-387-87862-1|page=21|ref=harv}}</ref><ref name="Skorokhod2005page42">{{cite book|author=Valeriy Skorokhod|title=Basic Principles and Applications of Probability Theory|url=https://books.google.com/books?id=dQkYMjRK3fYC|year= 2005|publisher=Springer Science & Business Media|isbn=978-3-540-26312-8|page=42}}</ref> because a stochastic process can also be interpreted as a random element in a [[function space]].<ref name="Kallenberg2002page24"/><ref name="Lamperti1977page1">{{cite book|author=John Lamperti|title=Stochastic processes: a survey of the mathematical theory|url=https://books.google.com/books?id=Pd4cvgAACAAJ|year=1977|publisher=Springer-Verlag|isbn=978-3-540-90275-1|pages=1–2}}</ref> The terms ''stochastic process'' and ''random process'' are used interchangeably, often with no specific [[mathematical space]] for the set that indexes the random variables.<ref name="Kallenberg2002page24">{{cite book|author=Olav Kallenberg|title=Foundations of Modern Probability|url=https://books.google.com/books?id=L6fhXh13OyMC|year=2002|publisher=Springer Science & Business Media|isbn=978-0-387-95313-7|pages=24–25}}</ref><ref name="ChaumontYor2012">{{cite book|author1=Loïc Chaumont|author2=Marc Yor|title=Exercises in Probability: A Guided Tour from Measure Theory to Random Processes, Via Conditioning|url=https://books.google.com/books?id=1dcqV9mtQloC&pg=PR4|year= 2012|publisher=Cambridge University Press|isbn=978-1-107-60655-5|page=175}}</ref> But often these two terms are used when the random variables are indexed by the [[integers]] or an [[Interval (mathematics)|interval]] of the [[real line]].<ref name="GikhmanSkorokhod1969page1"/><ref name="ChaumontYor2012"/> If the random variables are indexed by the [[Cartesian plane]] or some higher-dimensional [[Euclidean space]], then the collection of random variables is usually called a [[random field]] instead.<ref name="GikhmanSkorokhod1969page1"/><ref name="AdlerTaylor2009page7">{{cite book|author1=Robert J. Adler|author2=Jonathan E. Taylor|title=Random Fields and Geometry|url=https://books.google.com/books?id=R5BGvQ3ejloC|year=2009|publisher=Springer Science & Business Media|isbn=978-0-387-48116-6|pages=7–8}}</ref> The values of a stochastic process are not always numbers and can be vectors or other mathematical objects.<ref name="GikhmanSkorokhod1969page1"/><ref name="Lamperti1977page1"/>

Based on their mathematical properties, stochastic processes can be grouped into various categories, which include [[random walk]]s,<ref name="LawlerLimic2010">{{cite book|author1=Gregory F. Lawler|author2=Vlada Limic|title=Random Walk: A Modern Introduction|url=https://books.google.com/books?id=UBQdwAZDeOEC|year= 2010|publisher=Cambridge University Press|isbn=978-1-139-48876-1}}</ref> [[Martingale (probability theory)|martingales]],<ref name="Williams1991">{{cite book|author=David Williams|title=Probability with Martingales|url=https://books.google.com/books?id=e9saZ0YSi-AC|year=1991|publisher=Cambridge University Press|isbn=978-0-521-40605-5}}</ref> [[Markov process]]es,<ref name="RogersWilliams2000">{{cite book|author1=L. C. G. Rogers|author2=David Williams|title=Diffusions, Markov Processes, and Martingales: Volume 1, Foundations|url=https://books.google.com/books?id=W0ydAgAAQBAJ&pg=PA1|year= 2000|publisher=Cambridge University Press|isbn=978-1-107-71749-7}}</ref> [[Lévy process]]es,<ref name="ApplebaumBook2004">{{cite book|author=David Applebaum|title=Lévy Processes and Stochastic Calculus|url=https://books.google.com/books?id=q7eDUjdJxIkC|year=2004|publisher=Cambridge University Press|isbn=978-0-521-83263-2}}</ref> [[Gaussian process]]es,<ref>{{cite book|author=Mikhail Lifshits|title=Lectures on Gaussian Processes|url=https://books.google.com/books?id=03m2UxI-UYMC|year=2012|publisher=Springer Science & Business Media|isbn=978-3-642-24939-6}}</ref> random fields,<ref name="Adler2010">{{cite book|author=Robert J. Adler|title=The Geometry of Random Fields|url=https://books.google.com/books?id=ryejJmJAj28C&pg=PA1|year= 2010|publisher=SIAM|isbn=978-0-89871-693-1}}</ref> [[renewal process]]es, and [[branching process]]es.<ref name="KarlinTaylor2012">{{cite book|author1=Samuel Karlin|author2=Howard E. Taylor|title=A First Course in Stochastic Processes|url=https://books.google.com/books?id=dSDxjX9nmmMC|year= 2012|publisher=Academic Press|isbn=978-0-08-057041-9}}</ref> The study of stochastic processes uses mathematical knowledge and techniques from [[probability]], [[calculus]], [[linear algebra]], [[set theory]], and [[topology]]<ref name="Hajek2015">{{cite book|author=Bruce Hajek|title=Random Processes for Engineers|url=https://books.google.com/books?id=Owy0BgAAQBAJ|year=2015|publisher=Cambridge University Press|isbn=978-1-316-24124-0}}</ref><ref name="LatoucheRamaswami1999">{{cite book|author1=G. Latouche|author2=V. Ramaswami|title=Introduction to Matrix Analytic Methods in Stochastic Modeling|url=https://books.google.com/books?id=Kan2ki8jqzgC|year=1999|publisher=SIAM|isbn=978-0-89871-425-8}}</ref><ref name="DaleyVere-Jones2007">{{cite book|author1=D.J. Daley|author2=David Vere-Jones|title=An Introduction to the Theory of Point Processes: Volume II: General Theory and Structure|url=https://books.google.com/books?id=nPENXKw5kwcC|year= 2007|publisher=Springer Science & Business Media|isbn=978-0-387-21337-8}}</ref> as well as branches of [[mathematical analysis]] such as [[real analysis]], [[measure theory]], [[Fourier analysis]], and [[functional analysis]].<ref name="Billingsley2008">{{cite book|author=Patrick Billingsley|title=Probability and Measure|url=https://books.google.com/books?id=QyXqOXyxEeIC|year=2008|publisher=Wiley India Pvt. Limited|isbn=978-81-265-1771-8}}</ref><ref name="Brémaud2014">{{cite book|author=Pierre Brémaud|title=Fourier Analysis and Stochastic Processes|url=https://books.google.com/books?id=dP2JBAAAQBAJ&pg=PA1|year= 2014|publisher=Springer|isbn=978-3-319-09590-5}}</ref><ref name="Bobrowski2005">{{cite book|author=Adam Bobrowski|title=Functional Analysis for Probability and Stochastic Processes: An Introduction|url=https://books.google.com/books?id=q7dR3d5nqaUC|year= 2005|publisher=Cambridge University Press|isbn=978-0-521-83166-6}}</ref> The theory of stochastic processes is considered to be an important contribution to mathematics<ref name="Applebaum2004">{{cite journal|last1=Applebaum|first1=David|title=Lévy processes: From probability to finance and quantum groups|journal=Notices of the AMS|volume=51|issue=11|year=2004|pages=1336–1347}}</ref> and it continues to be an active topic of research for both theoretical reasons and applications.<ref name="BlathImkeller2011">{{cite book|author1=Jochen Blath|author2=Peter Imkeller|author3=Sylvie Rœlly|title=Surveys in Stochastic Processes|url=https://books.google.com/books?id=CyK6KAjwdYkC|year=2011|publisher=European Mathematical Society|isbn=978-3-03719-072-2}}</ref><ref name="Talagrand2014">{{cite book|author=Michel Talagrand|title=Upper and Lower Bounds for Stochastic Processes: Modern Methods and Classical Problems|url=https://books.google.com/books?id=tfa5BAAAQBAJ&pg=PR4|year=2014|publisher=Springer Science & Business Media|isbn=978-3-642-54075-2|pages=4–}}</ref><ref name="Bressloff2014VII">{{cite book|author=Paul C. Bressloff|title=Stochastic Processes in Cell Biology|url=https://books.google.com/books?id=SwZYBAAAQBAJ&pg=PA1|year=2014|publisher=Springer|isbn=978-3-319-08488-6|pages=vii–ix}}</ref>

根据随机过程的数学性质，随机过程可以分为不同的类别，包括[[随机游走]]s，<ref name=“LawlerLimic2010”>{cite book | author1=Gregory F.Lawler | author2=Vlada Limic | title=random walk:A Modern Introduction | url=https://books.google.com/books？id=UBQdwAZDeOEC | year=2010 | publisher=Cambridge University Press | isbn=978-1-139-48876-1}</ref>[[鞅（概率论）|鞅]]，<ref name=“Williams1991”>{cite book |作者=David Williams | title=probability with鞅| url=https://books.google.com/books？id=e9saZ0YSi AC | year=1991 | publisher=Cambridge University Press | isbn=978-0-521-40605-5}</ref>[[Markov process]]es，<ref name=“rogerswillams2000”>{引用图书| author1=L.C.G.Rogers | author2=David Williams | title=扩散，马尔可夫过程，和鞅：第一卷，基础|网址=https://books.google.com/books？id=W0ydAgAAQBAJ&pg=PA1 | year=2000 | publisher=Cambridge University Press | isbn=978-1-107-71749-7}</ref>[[Lévy process]]es，<ref name=“ApplebaumBook2004”>{cite book |作者=David Applebaum | title=Lévy过程和随机微积分| url=https://books.google.com/books？id=q7eDUjdJxIkC | year=2004 | publisher=Cambridge University Press | isbn=978-0-521-83263-2}</ref>[[Gaussian process]]es，<ref>{cite book | author=Mikhail Lifshits | title=关于高斯过程的讲座| url=https://books.google.com/books？id=03m2UxI UYMC | year=2012 | publisher=Springer Science&Business Media | isbn=978-3-642-24939-6}</ref>随机域，<ref name=“Adler2010”>{cite book | author=Robert J.Adler | title=The Geometry of random fields | url=https://books.google.com/books？id=ryejJmJAj28C&pg=PA1 | year=2010 | publisher=SIAM | isbn=978-0-89871-693-1}</ref>[[renewal process]]es，和[[branching process]]es.<ref name=“KarlinTaylor2012”>{引用图书| author1=Samuel Karlin | author2=Howard E.Taylor | title=随机过程的第一门课程| url=https://books.google.com/books？id=dSDxjX9nmmMC | year=2012 | publisher=academical Press | isbn=978-0-08-057041-9}</ref>随机过程的研究使用了[[概率]]、[[微积分]]、[[线性代数]]、[[集理论]]的数学知识和技术，和[[topology]]<ref name=“Hajek2015”>{cite book | author=Bruce Hajek | title=Random Processes for Engineers |网址=https://books.google.com/books？id=Owy0BgAAQBAJ | year=2015 | publisher=Cambridge University Press | isbn=978-1-316-24124-0}</ref><ref name=“LatoucheRamaswami1999”>{cite book | author1=G.Latouche | author2=V.Ramaswami | title=随机建模中的矩阵分析方法简介| url=https://books.google.com/books？id=Kan2ki8jqzgC | year=1999 | publisher=SIAM | isbn=978-0-89871-425-8}</ref><ref name=“DaleyVere-Jones 2007”>{引用图书| author1=D.J.Daley | author2=David Vere-Jones | title=点过程理论导论：第二卷：一般理论与结构| url=https://books.google.com/books？id=nPENXKw5kwcC | year=2007 | publisher=Springer Science&Business Media | isbn=978-0-387-21337-8}</ref>以及[[数学分析]]的分支，如[[真实分析]，[[测量理论]]，[[傅立叶分析]，和[[功能分析].<ref name=“Billingsley2008”>{cite book | author=Patrick Billingsley | title=Probability and Measure |网址=https://books.google.com/books？id=QyXqOXyxEeIC | year=2008 | publisher=Wiley India私人有限公司| isbn=978-81-265-1771-8}</ref><ref name=“Brémaud2014”>{cite book | author=Pierre Brémaud | title=Fourier Analysis and Random Processes |网址=https://books.google.com/books？id=dP2JBAAAQBAJ&pg=PA1 | year=2014 | publisher=Springer | isbn=978-3-319-09590-5}</ref><ref name=“Bobrowski2005”>{cite book | author=Adam Bobrowski | title=概率与随机过程的函数分析：简介|网址=https://books.google.com/books？id=q7dR3d5nqaUC | year=2005 | publisher=Cambridge University Press | isbn=978-0-521-83166-6}</ref>随机过程理论被认为是对数学的重要贡献AMS | volume=51 | issue=11 | year=2004 | pages=1336–1347}</ref>并且由于理论原因和应用，它仍然是一个活跃的研究课题。<ref name=“BlathImkeller2011”>{cite book | author1=Jochen Blath | author2=Peter Imkeller | author3=Sylvie Rœlly | title=Surveys in random Processes | url=https://books.google.com/books？id=CyK6KAjwdYkC | year=2011 | publisher=欧洲数学学会| isbn=978-3-03719-072-2}</ref><ref name=“Talagrand2014”>{引用图书|作者=Michel Talagrand | title=随机过程的上下界：现代方法和经典问题| url=https://books.google.com/books？id=tfa5baaqbaj&pg=PR4 | year=2014 | publisher=Springer Science&Business Media | isbn=978-3-642-54075-2 | pages=4–}</ref><ref name=“Bressloff2014VII”>{引用图书|作者=Paul C.Bressloff | title=细胞生物学中的随机过程|网址=https://books.google.com/books？id=SWZYBAAQBAJ&pg公司

A stochastic or random process can be defined as a collection of random variables that is indexed by some mathematical set, meaning that each random variable of the stochastic process is uniquely associated with an element in the set.

A stochastic or random process can be defined as a collection of random variables that is indexed by some mathematical set, meaning that each random variable of the stochastic process is uniquely associated with an element in the set.

When interpreted as time, if the index set of a stochastic process has a finite or countable number of elements, such as a finite set of numbers, the set of integers, or the natural numbers, then the stochastic process is said to be in discrete time. If the index set is some interval of the real line, then time is said to be continuous. The two types of stochastic processes are respectively referred to as discrete-time and continuous-time stochastic processes. Discrete-time stochastic processes are considered easier to study because continuous-time processes require more advanced mathematical techniques and knowledge, particularly due to the index set being uncountable. If the index set is the integers, or some subset of them, then the stochastic process can also be called a random sequence. In his work on probability Ars Conjectandi, originally published in Latin in 1713, Jakob Bernoulli used the phrase "Ars Conjectandi sive Stochastice", which has been translated to "the art of conjecturing or stochastics". This phrase was used, with reference to Bernoulli, by Ladislaus Bortkiewicz who in 1917 wrote in German the word stochastik with a sense meaning random. The term stochastic process first appeared in English in a 1934 paper by Joseph Doob. though the German term had been used earlier, for example, by Andrei Kolmogorov in 1931.

When interpreted as time, if the index set of a stochastic process has a finite or countable number of elements, such as a finite set of numbers, the set of integers, or the natural numbers, then the stochastic process is said to be in discrete time. If the index set is some interval of the real line, then time is said to be continuous. The two types of stochastic processes are respectively referred to as discrete-time and continuous-time stochastic processes. Discrete-time stochastic processes are considered easier to study because continuous-time processes require more advanced mathematical techniques and knowledge, particularly due to the index set being uncountable. If the index set is the integers, or some subset of them, then the stochastic process can also be called a random sequence. In his work on probability Ars Conjectandi, originally published in Latin in 1713, Jakob Bernoulli used the phrase "Ars Conjectandi sive Stochastice", which has been translated to "the art of conjecturing or stochastics". This phrase was used, with reference to Bernoulli, by Ladislaus Bortkiewicz who in 1917 wrote in German the word stochastik with a sense meaning random. The term stochastic process first appeared in English in a 1934 paper by Joseph Doob. though the German term had been used earlier, for example, by Andrei Kolmogorov in 1931.

当被解释为时间时，如果一个随机过程的指数集有一个有限或可数的元素数，如一个有限的数字集，一个整数集，或自然数，那么随机过程被称为在离散时间。如果索引集是实线的某个区间，那么时间就是连续的。这两类随机过程分别称为离散时间过程和连续时间过程。离散时间随机过程被认为更容易研究，因为连续时间过程需要更先进的数学技术和知识，特别是由于指数集是不可数的。如果索引集是整数，或者其中的一些子集，那么随机过程也可以被称为随机序列。雅各布 · 伯努利在1713年以拉丁文出版的《猜测概率论》一书中使用了“猜测随机论”这个短语，这个短语被翻译成了“猜测或推测的艺术”。1917年，拉迪斯劳斯·博特基威茨在德语中写下了“随机”一词，意思是随机。1934年，Joseph Doob 在一篇论文中首次提到随机过程这个词。尽管这个德语术语早在1931年就被安德烈 · 科尔莫哥罗夫使用过。

当被解释为时间时，如果一个随机过程的索引集的元素数量有限或可数，如一个有限的数字集，一个整数集，或自然数集，那么随机过程被称为在离散时间内。如果索引集是实线的某个区间，那么时间就是连续的。这两类随机过程分别称为离散时间过程和连续时间过程。离散时间随机过程被认为更容易研究，因为连续时间过程需要更先进的数学技术和知识，特别是由于索引集是不可数的。如果索引集是整数，或者整数的一些子集，那么随机过程也可以被称为'''<font color="#ff8000"> 随机序列Random sequence</font>'''。雅各布 · 伯努利在1713年以拉丁文出版的《猜测概率论》一书中使用了“猜测随机论”这个短语，这个短语被翻译成了“猜测或推测的艺术”。1917年，拉迪斯劳斯·博特基威茨在德语中写下了“随机”一词，意思是随机。1934年，Joseph Doob 在一篇论文中首次提到随机过程这个词。尽管这个德语术语早在1931年就被安德烈 · 科尔莫哥罗夫使用过。

A stochastic or random process can be defined as a collection of random variables that is indexed by some mathematical set, meaning that each random variable of the stochastic process is uniquely associated with an element in the set.<ref name="Parzen1999"/><ref name="GikhmanSkorokhod1969page1"/> The set used to index the random variables is called the '''index set'''. Historically, the index set was some [[subset]] of the [[real line]], such as the [[natural numbers]], giving the index set the interpretation of time.<ref name="doob1953stochasticP46to47"/> Each random variable in the collection takes values from the same [[mathematical space]] known as the '''state space'''. This state space can be, for example, the integers, the real line or <math>n</math>-dimensional Euclidean space.<ref name="doob1953stochasticP46to47"/><ref name="GikhmanSkorokhod1969page1"/> An '''increment''' is the amount that a stochastic process changes between two index values, often interpreted as two points in time.<ref name="KarlinTaylor2012page27"/><ref name="Applebaum2004page1337"/> A stochastic process can have many [[Outcome (probability)|outcomes]], due to its randomness, and a single outcome of a stochastic process is called, among other names, a '''sample function''' or '''realization'''.<ref name="Lamperti1977page1"/><ref name="RogersWilliams2000page121b"/>

A stochastic or random process can be defined as a collection of random variables that is indexed by some mathematical set, meaning that each random variable of the stochastic process is uniquely associated with an element in the set.<ref name="Parzen1999"/><ref name="GikhmanSkorokhod1969page1"/> The set used to index the random variables is called the '''index set'''. Historically, the index set was some [[subset]] of the [[real line]], such as the [[natural numbers]], giving the index set the interpretation of time.<ref name="doob1953stochasticP46to47"/> Each random variable in the collection takes values from the same [[mathematical space]] known as the '''state space'''. This state space can be, for example, the integers, the real line or <math>n</math>-dimensional Euclidean space.<ref name="doob1953stochasticP46to47"/><ref name="GikhmanSkorokhod1969page1"/> An '''increment''' is the amount that a stochastic process changes between two index values, often interpreted as two points in time.<ref name="KarlinTaylor2012page27"/><ref name="Applebaum2004page1337"/> A stochastic process can have many [[Outcome (probability)|outcomes]], due to its randomness, and a single outcome of a stochastic process is called, among other names, a '''sample function''' or '''realization'''.<ref name="Lamperti1977page1"/><ref name="RogersWilliams2000page121b"/>

According to the Oxford English Dictionary, early occurrences of the word random in English with its current meaning, which relates to chance or luck, date back to the 16th century, while earlier recorded usages started in the 14th century as a noun meaning "impetuosity, great speed, force, or violence (in riding, running, striking, etc.)". The word itself comes from a Middle French word meaning "speed, haste", and it is probably derived from a French verb meaning "to run" or "to gallop". The first written appearance of the term random process pre-dates stochastic process, which the Oxford English Dictionary also gives as a synonym, and was used in an article by Francis Edgeworth published in 1888.

According to the Oxford English Dictionary, early occurrences of the word random in English with its current meaning, which relates to chance or luck, date back to the 16th century, while earlier recorded usages started in the 14th century as a noun meaning "impetuosity, great speed, force, or violence (in riding, running, striking, etc.)". The word itself comes from a Middle French word meaning "speed, haste", and it is probably derived from a French verb meaning "to run" or "to gallop". The first written appearance of the term random process pre-dates stochastic process, which the Oxford English Dictionary also gives as a synonym, and was used in an article by Francis Edgeworth published in 1888.

根据牛津英语词典的研究，英语中随机这个词的早期出现和它现在的意思有关，可以追溯到16世纪，而早期记录的用法开始于14世纪，是一个名词，意思是“浮躁、极速、力量或暴力(在骑马、奔跑、惊人等等)”。这个单词本身来自中世纪法语单词，意思是“速度，匆忙” ，它可能来源于法语动词，意思是“奔跑”或“疾驰”。随机过程这个术语的第一次书面出现早于随机过程，牛津英语词典也把它作为同义词，并在 Francis Edgeworth 1888年发表的一篇文章中使用。

根据牛津英语词典的研究，英语中随机这个词的早期出现和它现在的意思有关，可以追溯到16世纪，而早期记录的用法开始于14世纪，是一个名词，意思是“浮躁、极速、力量或暴力(在骑马、奔跑、惊人等等)”。这个单词本身来自中世纪法语单词，意思是“速度，匆忙” ，它可能来源于法语动词，意思是“奔跑”或“疾驰”。随机（random）过程这个术语的第一次书面出现早于随机（stochastic）过程，牛津英语词典也把它作为同义词，并在 Francis Edgeworth 1888年发表的一篇文章中使用。

[[File:Wiener process 3d.png|thumb|right|A single computer-simulated '''sample function''' or '''realization''', among other terms, of a three-dimensional Wiener or Brownian motion process for time 0 ≤ t ≤ 2. The index set of this stochastic process is the non-negative numbers, while its state space is three-dimensional Euclidean space.]]

[[File:Wiener process 3d.png|thumb|right|A single computer-simulated '''sample function''' or '''realization''', among other terms, of a three-dimensional Wiener or Brownian motion process for time 0 ≤ t ≤ 2. The index set of this stochastic process is the non-negative numbers, while its state space is three-dimensional Euclidean space.]]

When interpreted as time, if the index set of a stochastic process has a finite or countable number of elements, such as a finite set of numbers, the set of integers, or the natural numbers, then the stochastic process is said to be in '''[[discrete time]]'''.<ref name="Billingsley2008page482"/><ref name="Borovkov2013page527">{{cite book|author=Alexander A. Borovkov|title=Probability Theory|url=https://books.google.com/books?id=hRk_AAAAQBAJ|year=2013|publisher=Springer Science & Business Media|isbn=978-1-4471-5201-9|page=527}}</ref> If the index set is some interval of the real line, then time is said to be '''[[continuous time|continuous]]'''. The two types of stochastic processes are respectively referred to as '''discrete-time''' and '''[[continuous-time stochastic process]]es'''.<ref name="KarlinTaylor2012page27"/><ref name="Brémaud2014page120"/><ref name="Rosenthal2006page177">{{cite book|author=Jeffrey S Rosenthal|title=A First Look at Rigorous Probability Theory|url=https://books.google.com/books?id=am1IDQAAQBAJ|year=2006|publisher=World Scientific Publishing Co Inc|isbn=978-981-310-165-4|pages=177–178}}</ref> Discrete-time stochastic processes are considered easier to study because continuous-time processes require more advanced mathematical techniques and knowledge, particularly due to the index set being uncountable.<ref name="KloedenPlaten2013page63">{{cite book|author1=Peter E. Kloeden|author2=Eckhard Platen|title=Numerical Solution of Stochastic Differential Equations|url=https://books.google.com/books?id=r9r6CAAAQBAJ=PA1|year=2013|publisher=Springer Science & Business Media|isbn=978-3-662-12616-5|page=63}}</ref><ref name="Khoshnevisan2006page153">{{cite book|author=Davar Khoshnevisan|title=Multiparameter Processes: An Introduction to Random Fields|url=https://books.google.com/books?id=XADpBwAAQBAJ|year=2006|publisher=Springer Science & Business Media|isbn=978-0-387-21631-7|pages=153–155}}</ref> If the index set is the integers, or some subset of them, then the stochastic process can also be called a '''random sequence'''.<ref name="Borovkov2013page527"/>

When interpreted as time, if the index set of a stochastic process has a finite or countable number of elements, such as a finite set of numbers, the set of integers, or the natural numbers, then the stochastic process is said to be in '''[[discrete time]]'''.<ref name="Billingsley2008page482"/><ref name="Borovkov2013page527">{{cite book|author=Alexander A. Borovkov|title=Probability Theory|url=https://books.google.com/books?id=hRk_AAAAQBAJ|year=2013|publisher=Springer Science & Business Media|isbn=978-1-4471-5201-9|page=527}}</ref> If the index set is some interval of the real line, then time is said to be '''[[continuous time|continuous]]'''. The two types of stochastic processes are respectively referred to as '''discrete-time''' and '''[[continuous-time stochastic process]]es'''.<ref name="KarlinTaylor2012page27"/><ref name="Brémaud2014page120"/><ref name="Rosenthal2006page177">{{cite book|author=Jeffrey S Rosenthal|title=A First Look at Rigorous Probability Theory|url=https://books.google.com/books?id=am1IDQAAQBAJ|year=2006|publisher=World Scientific Publishing Co Inc|isbn=978-981-310-165-4|pages=177–178}}</ref> Discrete-time stochastic processes are considered easier to study because continuous-time processes require more advanced mathematical techniques and knowledge, particularly due to the index set being uncountable.<ref name="KloedenPlaten2013page63">{{cite book|author1=Peter E. Kloeden|author2=Eckhard Platen|title=Numerical Solution of Stochastic Differential Equations|url=https://books.google.com/books?id=r9r6CAAAQBAJ=PA1|year=2013|publisher=Springer Science & Business Media|isbn=978-3-662-12616-5|page=63}}</ref><ref name="Khoshnevisan2006page153">{{cite book|author=Davar Khoshnevisan|title=Multiparameter Processes: An Introduction to Random Fields|url=https://books.google.com/books?id=XADpBwAAQBAJ|year=2006|publisher=Springer Science & Business Media|isbn=978-0-387-21631-7|pages=153–155}}</ref> If the index set is the integers, or some subset of them, then the stochastic process can also be called a '''random sequence'''.<ref name="Borovkov2013page527"/>

当解释为时间时，如果随机过程的指标集有有限个或可数个元素，例如有限的一组数、一组整数或自然数，那么随机过程被称为“'[[discrete time]]''”。<ref name=“Billingsley2008page482”/><ref name=“Borovkov2013page527”>{cite book | author=Alexander A.Borovkov | title=Probability Theory |网址=https://books.google.com/books？id=hRk_AAAAQBAJ | year=2013 | publisher=Springer Science&Business Media | isbn=978-1-4471-5201-9 | page=527}}</ref>如果索引集是实线的某个区间，则时间被称为“'[[continuous time|continuous]]”。这两类随机过程分别被称为“离散时间”和“[[连续时间随机过程]]es”。<ref name=“KarlinTaylor2012page27”/><ref name=“Brémaud2014page120”/><ref name=“Rosenthal2006page177”>{cite book | author=Jeffrey S Rosenthal | title=A First Look on critical Probability理论|网址=https://books.google.com/books？id=am1IDQAAQBAJ | year=2006 | publisher=World Scientific Publishing Co Inc | isbn=978-981-310-165-4 | pages=177-178}</ref>离散时间随机过程被认为更容易研究，因为连续时间过程需要更先进的数学技术和知识，特别是由于索引集是不可数的。<ref name=“KloedenPlaten2013page63”>{cite book | author1=Peter E.Kloeden | author2=Eckhard Platen | title=随机微分方程的数值解=https://books.google.com/books？id=r9r6CAAAQBAJ=PA1 | year=2013 | publisher=Springer Science&Business Media | isbn=978-3-662-12616-5 | page=63}</ref><ref name=“khoshnivesan2006page153”>{cite book | author=Davar khoshnivesan | title=多参数过程：随机字段简介| url=https://books.google.com/books？id=XADpBwAAQBAJ | year=2006 | publisher=Springer Science&Business Media | isbn=978-0-387-21631-7 | pages=153–155}</ref>如果索引集是整数或整数的子集，则随机过程也可以称为“随机序列”。<ref name=“Borovkov2013page527”/>

当解释为时间时，如果随机过程的指标集有有限个或可数个元素，例如有限的一组数、一组整数或自然数，那么随机过程被称为“'[[离散时间]]''”。<ref name=“Billingsley2008page482”/><ref name=“Borovkov2013page527”>{cite book | author=Alexander A.Borovkov | title=Probability Theory |网址=https://books.google.com/books？id=hRk_AAAAQBAJ | year=2013 | publisher=Springer Science&Business Media | isbn=978-1-4471-5201-9 | page=527}}</ref>如果索引集是实线的某个区间，则时间被称为“'[[continuous time|continuous]]”。这两类随机过程分别被称为“离散时间”和“[[连续时间随机过程]]es”。<ref name=“KarlinTaylor2012page27”/><ref name=“Brémaud2014page120”/><ref name=“Rosenthal2006page177”>{cite book | author=Jeffrey S Rosenthal | title=A First Look on critical Probability理论|网址=https://books.google.com/books？id=am1IDQAAQBAJ | year=2006 | publisher=World Scientific Publishing Co Inc | isbn=978-981-310-165-4 | pages=177-178}</ref>离散时间随机过程被认为更容易研究，因为连续时间过程需要更先进的数学技术和知识，特别是由于索引集是不可数的。<ref name=“KloedenPlaten2013page63”>{cite book | author1=Peter E.Kloeden | author2=Eckhard Platen | title=随机微分方程的数值解=https://books.google.com/books？id=r9r6CAAAQBAJ=PA1 | year=2013 | publisher=Springer Science&Business Media | isbn=978-3-662-12616-5 | page=63}</ref><ref name=“khoshnivesan2006page153”>{cite book | author=Davar khoshnivesan | title=多参数过程：随机字段简介| url=https://books.google.com/books？id=XADpBwAAQBAJ | year=2006 | publisher=Springer Science&Business Media | isbn=978-0-387-21631-7 | pages=153–155}</ref>如果索引集是整数或整数的子集，则随机过程也可以称为“随机序列”。<ref name=“Borovkov2013page527”/>

If the state space is the integers or natural numbers, then the stochastic process is called a '''discrete''' or '''integer-valued stochastic process'''. If the state space is the real line, then the stochastic process is referred to as a '''real-valued stochastic process''' or a '''process with continuous state space'''. If the state space is <math>n</math>-dimensional Euclidean space, then the stochastic process is called a <math>n</math>-'''dimensional vector process''' or <math>n</math>-'''vector process'''.<ref name="Florescu2014page294"/><ref name="KarlinTaylor2012page26"/>

If the state space is the integers or natural numbers, then the stochastic process is called a '''discrete''' or '''integer-valued stochastic process'''. If the state space is the real line, then the stochastic process is referred to as a '''real-valued stochastic process''' or a '''process with continuous state space'''. If the state space is <math>n</math>-dimensional Euclidean space, then the stochastic process is called a <math>n</math>-'''dimensional vector process''' or <math>n</math>-'''vector process'''.<ref name="Florescu2014page294"/><ref name="KarlinTaylor2012page26"/>

Random walks are stochastic processes that are usually defined as sums of iid random variables or random vectors in Euclidean space, so they are processes that change in discrete time. But some also use the term to refer to processes that change in continuous time, particularly the Wiener process used in finance, which has led to some confusion, resulting in its criticism. There are other various types of random walks, defined so their state spaces can be other mathematical objects, such as lattices and groups, and in general they are highly studied and have many applications in different disciplines.

Random walks are stochastic processes that are usually defined as sums of iid random variables or random vectors in Euclidean space, so they are processes that change in discrete time. But some also use the term to refer to processes that change in continuous time, particularly the Wiener process used in finance, which has led to some confusion, resulting in its criticism. There are other various types of random walks, defined so their state spaces can be other mathematical objects, such as lattices and groups, and in general they are highly studied and have many applications in different disciplines.

A classic example of a random walk is known as the simple random walk, which is a stochastic process in discrete time with the integers as the state space, and is based on a Bernoulli process, where each Bernoulli variable takes either the value positive one or negative one. In other words, the simple random walk takes place on the integers, and its value increases by one with probability, say, <math>p</math>, or decreases by one with probability <math>1-p</math>, so the index set of this random walk is the natural numbers, while its state space is the integers. If the <math>p=0.5</math>, this random walk is called a symmetric random walk.

A classic example of a random walk is known as the simple random walk, which is a stochastic process in discrete time with the integers as the state space, and is based on a Bernoulli process, where each Bernoulli variable takes either the value positive one or negative one. In other words, the simple random walk takes place on the integers, and its value increases by one with probability, say, <math>p</math>, or decreases by one with probability <math>1-p</math>, so the index set of this random walk is the natural numbers, while its state space is the integers. If the <math>p=0.5</math>, this random walk is called a symmetric random walk.

一个经典的随机游走的例子被称为简单随机游走，这是一个以整数为状态空间的离散时间随机过程，它基于一个伯努利过程，其中每个 Bernoulli 变量要么取值为正，要么取值为负。换句话说，简单随机游动发生在整数上，它的值随概率的增加而增加1，或随概率的减少而减少1，所以这种随机游动的指数集是自然数，而它的状态空间是整数。如果 < math > p = 0.5 </math > ，这种随机漫步称为对称随机漫步。

一个经典的'''<font color="#ff8000"> 随机游走Random walk</font>'''的例子被称为简单随机游走，这是一个以整数为状态空间的离散时间随机过程，它基于一个伯努利过程，其中每个 Bernoulli 变量要么取值为正，要么取值为负。换句话说，简单随机游动发生在整数上，它的值随概率的增加而增加1，或随概率的减少而减少1，所以这种'''<font color="#ff8000"> 随机游走Random walk</font>'''的索引集是自然数，而它的状态空间是整数。如果 < math > p = 0.5 </math > ，这种随机漫步称为'''<font color="#ff8000"> 对称随机游走Symmetric Random walk</font>'''。

The word ''stochastic'' in [[English language|English]] was originally used as an adjective with the definition "pertaining to conjecturing", and stemming from a [[Greek language|Greek]] word meaning "to aim at a mark, guess", and the [[Oxford English Dictionary]] gives the year 1662 as its earliest occurrence.<ref name="OxfordStochastic">{{Cite OED|Stochastic}}</ref> In his work on probability ''Ars Conjectandi'', originally published in Latin in 1713, [[Jakob Bernoulli]] used the phrase "Ars Conjectandi sive Stochastice", which has been translated to "the art of conjecturing or stochastics".<ref name="Sheĭnin2006page5">{{cite book|author=O. B. Sheĭnin|title=Theory of probability and statistics as exemplified in short dictums|url=https://books.google.com/books?id=XqMZAQAAIAAJ|year=2006|publisher=NG Verlag|isbn=978-3-938417-40-9|page=5}}</ref> This phrase was used, with reference to Bernoulli, by [[Ladislaus Bortkiewicz]]<ref name="SheyninStrecker2011page136">{{cite book|author1=Oscar Sheynin|author2=Heinrich Strecker|title=Alexandr A. Chuprov: Life, Work, Correspondence|url=https://books.google.com/books?id=1EJZqFIGxBIC&pg=PA9|year=2011|publisher=V&R unipress GmbH|isbn=978-3-89971-812-6|page=136}}</ref> who in 1917 wrote in German the word ''stochastik'' with a sense meaning random. The term ''stochastic process'' first appeared in English in a 1934 paper by [[Joseph Doob]].<ref name="OxfordStochastic"/> For the term and a specific mathematical definition, Doob cited another 1934 paper, where the term ''stochastischer Prozeß'' was used in German by [[Aleksandr Khinchin]],<ref name="Doob1934"/><ref name="Khintchine1934">{{cite journal|last1=Khintchine|first1=A.|title=Korrelationstheorie der stationeren stochastischen Prozesse|journal=Mathematische Annalen|volume=109|issue=1|year=1934|pages=604–615|issn=0025-5831|doi=10.1007/BF01449156}}</ref> though the German term had been used earlier, for example, by Andrei Kolmogorov in 1931.<ref name="Kolmogoroff1931page1">{{cite journal|last1=Kolmogoroff|first1=A.|title=Über die analytischen Methoden in der Wahrscheinlichkeitsrechnung|journal=Mathematische Annalen|volume=104|issue=1|year=1931|page=1|issn=0025-5831|doi=10.1007/BF01457949}}</ref>

The word ''stochastic'' in [[English language|English]] was originally used as an adjective with the definition "pertaining to conjecturing", and stemming from a [[Greek language|Greek]] word meaning "to aim at a mark, guess", and the [[Oxford English Dictionary]] gives the year 1662 as its earliest occurrence.<ref name="OxfordStochastic">{{Cite OED|Stochastic}}</ref> In his work on probability ''Ars Conjectandi'', originally published in Latin in 1713, [[Jakob Bernoulli]] used the phrase "Ars Conjectandi sive Stochastice", which has been translated to "the art of conjecturing or stochastics".<ref name="Sheĭnin2006page5">{{cite book|author=O. B. Sheĭnin|title=Theory of probability and statistics as exemplified in short dictums|url=https://books.google.com/books?id=XqMZAQAAIAAJ|year=2006|publisher=NG Verlag|isbn=978-3-938417-40-9|page=5}}</ref> This phrase was used, with reference to Bernoulli, by [[Ladislaus Bortkiewicz]]<ref name="SheyninStrecker2011page136">{{cite book|author1=Oscar Sheynin|author2=Heinrich Strecker|title=Alexandr A. Chuprov: Life, Work, Correspondence|url=https://books.google.com/books?id=1EJZqFIGxBIC&pg=PA9|year=2011|publisher=V&R unipress GmbH|isbn=978-3-89971-812-6|page=136}}</ref> who in 1917 wrote in German the word ''stochastik'' with a sense meaning random. The term ''stochastic process'' first appeared in English in a 1934 paper by [[Joseph Doob]].<ref name="OxfordStochastic"/> For the term and a specific mathematical definition, Doob cited another 1934 paper, where the term ''stochastischer Prozeß'' was used in German by [[Aleksandr Khinchin]],<ref name="Doob1934"/><ref name="Khintchine1934">{{cite journal|last1=Khintchine|first1=A.|title=Korrelationstheorie der stationeren stochastischen Prozesse|journal=Mathematische Annalen|volume=109|issue=1|year=1934|pages=604–615|issn=0025-5831|doi=10.1007/BF01449156}}</ref> though the German term had been used earlier, for example, by Andrei Kolmogorov in 1931.<ref name="Kolmogoroff1931page1">{{cite journal|last1=Kolmogoroff|first1=A.|title=Über die analytischen Methoden in der Wahrscheinlichkeitsrechnung|journal=Mathematische Annalen|volume=104|issue=1|year=1931|page=1|issn=0025-5831|doi=10.1007/BF01457949}}</ref>

The Wiener process is a stochastic process with stationary and independent increments that are normally distributed based on the size of the increments. The Wiener process is named after Norbert Wiener, who proved its mathematical existence, but the process is also called the Brownian motion process or just Brownian motion due to its historical connection as a model for Brownian movement in liquids.

The Wiener process is a stochastic process with stationary and independent increments that are normally distributed based on the size of the increments. The Wiener process is named after Norbert Wiener, who proved its mathematical existence, but the process is also called the Brownian motion process or just Brownian motion due to its historical connection as a model for Brownian movement in liquids.

维纳过程是一个具有平稳和独立增量的随机过程过程，这些增量是基于增量大小的正态分布。维纳过程是以诺伯特 · 维纳的名字命名的，他证明了维纳过程的数学存在性。

'''<font color="#ff8000"> 维纳过程Wiener process</font>'''是一个具有平稳和独立增量的随机过程，这些增量是基于增量大小的正态分布。维纳过程是以诺伯特 · 维纳的名字命名的，他证明了维纳过程的数学存在性。

The definition of a stochastic process varies,<ref name="FristedtGray2013page580">{{cite book|author1=Bert E. Fristedt|author2=Lawrence F. Gray|title=A Modern Approach to Probability Theory|url=https://books.google.com/books?id=9xT3BwAAQBAJ&pg=PA716|year= 2013|publisher=Springer Science & Business Media|isbn=978-1-4899-2837-5|page=580}}</ref> but a stochastic process is traditionally defined as a collection of random variables indexed by some set.<ref name="RogersWilliams2000page121"/><ref name="Asmussen2003page408"/> The terms ''random process'' and ''stochastic process'' are considered synonyms and are used interchangeably, without the index set being precisely specified.<ref name="Kallenberg2002page24"/><ref name="ChaumontYor2012"/><ref name="AdlerTaylor2009page7"/><ref name="Stirzaker2005page45">{{cite book|author=David Stirzaker|title=Stochastic Processes and Models|url=https://books.google.com/books?id=0avUelS7e7cC|year=2005|publisher=Oxford University Press|isbn=978-0-19-856814-8|page=45}}</ref><ref name="Rosenblatt1962page91">{{cite book|author=Murray Rosenblatt|title=Random Processes|url=https://archive.org/details/randomprocesses00rose_0|url-access=registration|year=1962|publisher=Oxford University Press|page=[https://archive.org/details/randomprocesses00rose_0/page/91 91]}}</ref><ref name="Gubner2006page383">{{cite book|author=John A. Gubner|title=Probability and Random Processes for Electrical and Computer Engineers|url=https://books.google.com/books?id=pa20eZJe4LIC|year=2006|publisher=Cambridge University Press|isbn=978-1-139-45717-0|page=383}}</ref> Both "collection",<ref name="Lamperti1977page1"/><ref name="Stirzaker2005page45"/> or "family" are used<ref name="Parzen1999"/><ref name="Ito2006page13">{{cite book|author=Kiyosi Itō|title=Essentials of Stochastic Processes|url=https://books.google.com/books?id=pY5_DkvI-CcC&pg=PR4|year=2006|publisher=American Mathematical Soc.|isbn=978-0-8218-3898-3|page=13}}</ref> while instead of "index set", sometimes the terms "parameter set"<ref name="Lamperti1977page1"/> or "parameter space"<ref name="AdlerTaylor2009page7"/> are used.

The definition of a stochastic process varies,<ref name="FristedtGray2013page580">{{cite book|author1=Bert E. Fristedt|author2=Lawrence F. Gray|title=A Modern Approach to Probability Theory|url=https://books.google.com/books?id=9xT3BwAAQBAJ&pg=PA716|year= 2013|publisher=Springer Science & Business Media|isbn=978-1-4899-2837-5|page=580}}</ref> but a stochastic process is traditionally defined as a collection of random variables indexed by some set.<ref name="RogersWilliams2000page121"/><ref name="Asmussen2003page408"/> The terms ''random process'' and ''stochastic process'' are considered synonyms and are used interchangeably, without the index set being precisely specified.<ref name="Kallenberg2002page24"/><ref name="ChaumontYor2012"/><ref name="AdlerTaylor2009page7"/><ref name="Stirzaker2005page45">{{cite book|author=David Stirzaker|title=Stochastic Processes and Models|url=https://books.google.com/books?id=0avUelS7e7cC|year=2005|publisher=Oxford University Press|isbn=978-0-19-856814-8|page=45}}</ref><ref name="Rosenblatt1962page91">{{cite book|author=Murray Rosenblatt|title=Random Processes|url=https://archive.org/details/randomprocesses00rose_0|url-access=registration|year=1962|publisher=Oxford University Press|page=[https://archive.org/details/randomprocesses00rose_0/page/91 91]}}</ref><ref name="Gubner2006page383">{{cite book|author=John A. Gubner|title=Probability and Random Processes for Electrical and Computer Engineers|url=https://books.google.com/books?id=pa20eZJe4LIC|year=2006|publisher=Cambridge University Press|isbn=978-1-139-45717-0|page=383}}</ref> Both "collection",<ref name="Lamperti1977page1"/><ref name="Stirzaker2005page45"/> or "family" are used<ref name="Parzen1999"/><ref name="Ito2006page13">{{cite book|author=Kiyosi Itō|title=Essentials of Stochastic Processes|url=https://books.google.com/books?id=pY5_DkvI-CcC&pg=PR4|year=2006|publisher=American Mathematical Soc.|isbn=978-0-8218-3898-3|page=13}}</ref> while instead of "index set", sometimes the terms "parameter set"<ref name="Lamperti1977page1"/> or "parameter space"<ref name="AdlerTaylor2009page7"/> are used.

Realizations of Wiener processes (or Brownian motion processes) with drift () and without drift ().

Realizations of Wiener processes (or Brownian motion processes) with drift () and without drift ().

带漂移()和无漂移()的 Wiener 过程(或布朗运动过程)的实现。

带漂移()和无漂移()的 '''<font color="#ff8000"> 维纳过程Wiener process</font>'''(或布朗运动过程)的实现。

The term ''random function'' is also used to refer to a stochastic or random process,<ref name="GikhmanSkorokhod1969page1"/><ref name="Loeve1978">{{cite book|author=M. Loève|title=Probability Theory II|url=https://books.google.com/books?id=1y229yBbULIC|year=1978|publisher=Springer Science & Business Media|isbn=978-0-387-90262-3|page=163}}</ref><ref name="Brémaud2014page133">{{cite book|author=Pierre Brémaud|title=Fourier Analysis and Stochastic Processes|url=https://books.google.com/books?id=dP2JBAAAQBAJ&pg=PA1|year=2014|publisher=Springer|isbn=978-3-319-09590-5|page=133}}</ref> though sometimes it is only used when the stochastic process takes real values.<ref name="Lamperti1977page1"/><ref name="Ito2006page13"/> This term is also used when the index sets are mathematical spaces other than the real line,<ref name="GikhmanSkorokhod1969page1"/><ref name="GusakKukush2010page1">{{harvtxt|Gusak|Kukush|Kulik|Mishura|2010}}, p. 1</ref> while the terms ''stochastic process'' and ''random process'' are usually used when the index set is interpreted as time,<ref name="GikhmanSkorokhod1969page1"/><ref name="GusakKukush2010page1"/><ref name="Bass2011page1">{{cite book|author=Richard F. Bass|title=Stochastic Processes|url=https://books.google.com/books?id=Ll0T7PIkcKMC|year=2011|publisher=Cambridge University Press|isbn=978-1-139-50147-7|page=1}}</ref> and other terms are used such as ''random field'' when the index set is <math>n</math>-dimensional Euclidean space <math>\mathbb{R}^n</math> or a [[manifold]].<ref name="GikhmanSkorokhod1969page1"/><ref name="Lamperti1977page1"/><ref name="AdlerTaylor2009page7"/>

The term ''random function'' is also used to refer to a stochastic or random process,<ref name="GikhmanSkorokhod1969page1"/><ref name="Loeve1978">{{cite book|author=M. Loève|title=Probability Theory II|url=https://books.google.com/books?id=1y229yBbULIC|year=1978|publisher=Springer Science & Business Media|isbn=978-0-387-90262-3|page=163}}</ref><ref name="Brémaud2014page133">{{cite book|author=Pierre Brémaud|title=Fourier Analysis and Stochastic Processes|url=https://books.google.com/books?id=dP2JBAAAQBAJ&pg=PA1|year=2014|publisher=Springer|isbn=978-3-319-09590-5|page=133}}</ref> though sometimes it is only used when the stochastic process takes real values.<ref name="Lamperti1977page1"/><ref name="Ito2006page13"/> This term is also used when the index sets are mathematical spaces other than the real line,<ref name="GikhmanSkorokhod1969page1"/><ref name="GusakKukush2010page1">{{harvtxt|Gusak|Kukush|Kulik|Mishura|2010}}, p. 1</ref> while the terms ''stochastic process'' and ''random process'' are usually used when the index set is interpreted as time,<ref name="GikhmanSkorokhod1969page1"/><ref name="GusakKukush2010page1"/><ref name="Bass2011page1">{{cite book|author=Richard F. Bass|title=Stochastic Processes|url=https://books.google.com/books?id=Ll0T7PIkcKMC|year=2011|publisher=Cambridge University Press|isbn=978-1-139-50147-7|page=1}}</ref> and other terms are used such as ''random field'' when the index set is <math>n</math>-dimensional Euclidean space <math>\mathbb{R}^n</math> or a [[manifold]].<ref name="GikhmanSkorokhod1969page1"/><ref name="Lamperti1977page1"/><ref name="AdlerTaylor2009page7"/>

术语“随机函数”也用于指随机或随机过程，<ref name=“GikhmanSkorokhod1969page1”/><ref name=“Loeve1978”>{cite book | author=M.Loève|title=Probability Theory II | url=https://books.google.com/books？id=1y229ybulic | year=1978 | publisher=Springer Science&Business Media | isbn=978-0-387-90262-3 | page=163}</ref><ref name=“Brémaud2014page133”>{cite book |作者=Pierre Brémaud | title=Fourier Analysis and randocial Processes |网址=https://books.google.com/books？id=dP2JBAAAQBAJ&pg=PA1 | year=2014 | publisher=Springer | isbn=978-3-319-09590-5 | page=133}</ref>尽管有时它只在随机过程取实值时使用。<ref name=“Lamperti1977page1”/><ref name=“Ito2006page13”/>当索引集是数学空间而不是实线时，也使用这个术语，<ref name=“GikhmanSkorokhod1969page1”/><ref name=“gusakkush2010page1”>{harvxt | Gusak | Kukush | Kulik | Mishura | 2010}，p.1</ref>，而术语“随机过程”和“随机过程”通常在指数集被解释为时间时使用，<ref name=“GikhmanSkorokhod1969page1”/><ref name=“GusakKukush2010page1”/><ref name=“Bass2011page1”>{引用图书|作者=Richard F.Bass | title=随机过程| url=https://books.google.com/books？id=Ll0T7PIkcKMC | year=2011 | publisher=Cambridge University Press | isbn=978-1-139-50147-7 | page=1}</ref>和其他术语，例如当索引集是<math>n</math>-维欧几里德空间<math>\mathbb{R}^n</math>或[[流形]].<ref name=“GikhmanSkorokhod1969page1”/><ref name=“Lamperti1977page1”/><ref name=“GikhmanSkorokhod1969page1”/><ref name=“Lamperti1977page1”/>name=“adlertaylor2009第7页”/>

术语'''<font color="#ff8000"> “随机函数”Random function</font>'''也用于指随机或随机过程，<ref name=“GikhmanSkorokhod1969page1”/><ref name=“Loeve1978”>{cite book | author=M.Loève|title=Probability Theory II | url=https://books.google.com/books？id=1y229ybulic | year=1978 | publisher=Springer Science&Business Media | isbn=978-0-387-90262-3 | page=163}</ref><ref name=“Brémaud2014page133”>{cite book |作者=Pierre Brémaud | title=Fourier Analysis and randocial Processes |网址=https://books.google.com/books？id=dP2JBAAAQBAJ&pg=PA1 | year=2014 | publisher=Springer | isbn=978-3-319-09590-5 | page=133}</ref>尽管有时它只在随机过程取实值时使用。<ref name=“Lamperti1977page1”/><ref name=“Ito2006page13”/>当索引集是数学空间而不是实线时，也使用这个术语，<ref name=“GikhmanSkorokhod1969page1”/><ref name=“gusakkush2010page1”>{harvxt | Gusak | Kukush | Kulik | Mishura | 2010}，p.1</ref>，而术语“随机过程”和“随机过程”通常在指数集被解释为时间时使用，<ref name=“GikhmanSkorokhod1969page1”/><ref name=“GusakKukush2010page1”/><ref name=“Bass2011page1”>{引用图书|作者=Richard F.Bass | title=随机过程| url=https://books.google.com/books？id=Ll0T7PIkcKMC | year=2011 | publisher=Cambridge University Press | isbn=978-1-139-50147-7 | page=1}</ref>和其他术语，例如当索引集是<math>n</math>-维欧几里德空间<math>\mathbb{R}^n</math>或[[流形]].<ref name=“GikhmanSkorokhod1969page1”/><ref name=“Lamperti1977page1”/><ref name=“GikhmanSkorokhod1969page1”/><ref name=“Lamperti1977page1”/>name=“adlertaylor2009第7页”/>

Playing a central role in the theory of probability, the Wiener process is often considered the most important and studied stochastic process, with connections to other stochastic processes. Its index set and state space are the non-negative numbers and real numbers, respectively, so it has both continuous index set and states space. But the process can be defined more generally so its state space can be <math>n</math>-dimensional Euclidean space. If the mean of any increment is zero, then the resulting Wiener or Brownian motion process is said to have zero drift. If the mean of the increment for any two points in time is equal to the time difference multiplied by some constant <math> \mu</math>, which is a real number, then the resulting stochastic process is said to have drift <math> \mu</math>.

Playing a central role in the theory of probability, the Wiener process is often considered the most important and studied stochastic process, with connections to other stochastic processes. Its index set and state space are the non-negative numbers and real numbers, respectively, so it has both continuous index set and states space. But the process can be defined more generally so its state space can be <math>n</math>-dimensional Euclidean space. If the mean of any increment is zero, then the resulting Wiener or Brownian motion process is said to have zero drift. If the mean of the increment for any two points in time is equal to the time difference multiplied by some constant <math> \mu</math>, which is a real number, then the resulting stochastic process is said to have drift <math> \mu</math>.

===Notation符号===

===Notation符号===

Almost surely, a sample path of a Wiener process is continuous everywhere but nowhere differentiable. It can be considered as a continuous version of the simple random walk. The process arises as the mathematical limit of other stochastic processes such as certain random walks rescaled, which is the subject of Donsker's theorem or invariance principle, also known as the functional central limit theorem.

Almost surely, a sample path of a Wiener process is continuous everywhere but nowhere differentiable. It can be considered as a continuous version of the simple random walk. The process arises as the mathematical limit of other stochastic processes such as certain random walks rescaled, which is the subject of Donsker's theorem or invariance principle, also known as the functional central limit theorem.

几乎可以肯定，Wiener 过程的样本路径在任何地方都是连续的，但是没有可微的地方。它可以看作是简单随机游动的连续形式。这个过程作为其他随机过程的数学极限出现，例如某些随机游动的重新标度，这是 Donsker 定理或不变性原理的主题，也被称为函数中心极限定理。

几乎可以肯定，'''<font color="#ff8000"> 维纳过程Wiener process</font>'''的样本路径在任何地方都是连续的，但是没有可微的地方。它可以看作是简单随机游走的连续形式。这个过程作为其他随机过程的数学极限出现，例如某些随机游动的重新标度，这是 Donsker 定理或不变性原理的主题，也被称为函数中心极限定理。

==Examples示例==

==Examples示例==

The Wiener process is a member of some important families of stochastic processes, including Markov processes, Lévy processes and Gaussian processes. It plays a central role in quantitative finance, where it is used, for example, in the Black–Scholes–Merton model. The process is also used in different fields, including the majority of natural sciences as well as some branches of social sciences, as a mathematical model for various random phenomena.

The Wiener process is a member of some important families of stochastic processes, including Markov processes, Lévy processes and Gaussian processes. It plays a central role in quantitative finance, where it is used, for example, in the Black–Scholes–Merton model. The process is also used in different fields, including the majority of natural sciences as well as some branches of social sciences, as a mathematical model for various random phenomena.

Wiener 过程是马尔可夫过程、 Lévy 过程和 Gaussian 过程等重要随机过程的一个成员。它在定量金融学中扮演着核心角色，例如，在布莱克-斯科尔斯-默顿模型中就使用了它。这个过程也用于不同的领域，包括大多数自然科学和一些社会科学分支，作为各种随机现象的数学模型。

'''<font color="#ff8000"> 维纳过程Wiener process</font>'''是马尔可夫过程、 Lévy 过程和 Gaussian 过程等重要随机过程的一个成员。它在定量金融学中扮演着核心角色，例如，在'''<font color="#ff8000"> 布莱克-斯科尔斯-默顿模型Black–Scholes–Merton model</font>'''中就使用了它。这个过程也用于不同的领域，包括大多数自然科学和一些社会科学分支，作为各种随机现象的数学模型。

===Bernoulli process伯努利过程===

==='''<font color="#ff8000"> Bernoulli process伯努利过程</font>'''===

{{Main|Bernoulli process}}

{{Main|Bernoulli process}}

One of the simplest stochastic processes is the [[Bernoulli process]],<ref name="Florescu2014page293"/> which is a sequence of [[independent and identically distributed]] (iid) random variables, where each random variable takes either the value one or zero, say one with probability <math>p</math> and zero with probability <math>1-p</math>. This process can be linked to repeatedly flipping a coin, where the probability of obtaining a head is <math>p</math> and its value is one, while the value of a tail is zero.<ref name="Florescu2014page301">{{cite book|author=Ionut Florescu|title=Probability and Stochastic Processes|url=https://books.google.com/books?id=Z5xEBQAAQBAJ&pg=PR22|year=2014|publisher=John Wiley & Sons|isbn=978-1-118-59320-2|page=301}}</ref> In other words, a Bernoulli process is a sequence of [[Independent and identically distributed random variables|iid]] Bernoulli random variables,<ref name="BertsekasTsitsiklis2002page273">{{cite book|author1=Dimitri P. Bertsekas|author2=John N. Tsitsiklis|title=Introduction to Probability|url=https://books.google.com/books?id=bcHaAAAAMAAJ|year=2002|publisher=Athena Scientific|isbn=978-1-886529-40-3|page=273}}</ref> where each coin flip is an example of a [[Bernoulli trial]].<ref name="Ibe2013page11">{{cite book|author=Oliver C. Ibe|title=Elements of Random Walk and Diffusion Processes|url=https://books.google.com/books?id=DUqaAAAAQBAJ&pg=PT10|year=2013|publisher=John Wiley & Sons|isbn=978-1-118-61793-9|page=11}}</ref>

One of the simplest stochastic processes is the [[Bernoulli process]],<ref name="Florescu2014page293"/> which is a sequence of [[independent and identically distributed]] (iid) random variables, where each random variable takes either the value one or zero, say one with probability <math>p</math> and zero with probability <math>1-p</math>. This process can be linked to repeatedly flipping a coin, where the probability of obtaining a head is <math>p</math> and its value is one, while the value of a tail is zero.<ref name="Florescu2014page301">{{cite book|author=Ionut Florescu|title=Probability and Stochastic Processes|url=https://books.google.com/books?id=Z5xEBQAAQBAJ&pg=PR22|year=2014|publisher=John Wiley & Sons|isbn=978-1-118-59320-2|page=301}}</ref> In other words, a Bernoulli process is a sequence of [[Independent and identically distributed random variables|iid]] Bernoulli random variables,<ref name="BertsekasTsitsiklis2002page273">{{cite book|author1=Dimitri P. Bertsekas|author2=John N. Tsitsiklis|title=Introduction to Probability|url=https://books.google.com/books?id=bcHaAAAAMAAJ|year=2002|publisher=Athena Scientific|isbn=978-1-886529-40-3|page=273}}</ref> where each coin flip is an example of a [[Bernoulli trial]].<ref name="Ibe2013page11">{{cite book|author=Oliver C. Ibe|title=Elements of Random Walk and Diffusion Processes|url=https://books.google.com/books?id=DUqaAAAAQBAJ&pg=PT10|year=2013|publisher=John Wiley & Sons|isbn=978-1-118-61793-9|page=11}}</ref>

最简单的随机过程之一是[[Bernoulli process]]，<ref name=“Florescu2014page293”/>它是[[独立且相同分布]]（iid）随机变量的序列，其中每个随机变量取1或0，比如概率<math>p</math>的值为1，概率<math>1-p</math>为零。这个过程可以与反复翻动硬币有关，其中获得头部的概率为<math>p</math>，其值为1，而尾部的值为零=https://books.google.com/books？id=z5sebqaaqbaj&pg=PR22 | year=2014 | publisher=John Wiley&Sons | isbn=978-1-118-59320-2 | page=301}</ref>换句话说，伯努利过程是一个[[独立且同分布随机变量| iid]]伯努利随机变量的序列，<ref name=“Bertsekatsitsiklis2002page273”>{cite book | author1=Dimitri P.Bertsekas | author2=John N.Tsitsiklis | title=概率简介| url=https://books.google.com/books？id=bcHaAAAAMAAJ | year=2002 | publisher=Athena Scientific | isbn=978-1-886529-40-3 | page=273}</ref>每一次抛硬币都是[[Bernoulli审判]]的一个例子。<ref name=“Ibe2013page11”>{cite book | author=Oliver C.Ibe | title=Elements of Random Walk and Diffusion Processes |网址=https://books.google.com/books？id=duqaaaaqbaj&pg=PT10 |年份=2013 | publisher=John Wiley&Sons | isbn=978-1-118-61793-9 | page=11}</ref>

最简单的随机过程之一是[[伯努利过程]]，<ref name=“Florescu2014page293”/>它是[[独立且相同分布]]（iid）随机变量的序列，其中每个随机变量取1或0，比如概率<math>p</math>的值为1，概率<math>1-p</math>为零。这个过程可以与反复翻动硬币有关，其中获得头部的概率为<math>p</math>，其值为1，而尾部的值为零=https://books.google.com/books？id=z5sebqaaqbaj&pg=PR22 | year=2014 | publisher=John Wiley&Sons | isbn=978-1-118-59320-2 | page=301}</ref>换句话说，伯努利过程是一个[[独立且同分布随机变量| iid]]伯努利随机变量的序列，<ref name=“Bertsekatsitsiklis2002page273”>{cite book | author1=Dimitri P.Bertsekas | author2=John N.Tsitsiklis | title=概率简介| url=https://books.google.com/books？id=bcHaAAAAMAAJ | year=2002 | publisher=Athena Scientific | isbn=978-1-886529-40-3 | page=273}</ref>每一次抛硬币都是[[Bernoulli审判]]的一个例子。<ref name=“Ibe2013page11”>{cite book | author=Oliver C.Ibe | title=Elements of Random Walk and Diffusion Processes |网址=https://books.google.com/books？id=duqaaaaqbaj&pg=PT10 |年份=2013 | publisher=John Wiley&Sons | isbn=978-1-118-61793-9 | page=11}</ref>

The Poisson process is a stochastic process that has different forms and definitions. It can be defined as a counting process, which is a stochastic process that represents the random number of points or events up to some time. The number of points of the process that are located in the interval from zero to some given time is a Poisson random variable that depends on that time and some parameter. This process has the natural numbers as its state space and the non-negative numbers as its index set. This process is also called the Poisson counting process, since it can be interpreted as an example of a counting process. The homogeneous Poisson process is a member of important classes of stochastic processes such as Markov processes and Lévy processes. If the parameter constant of the Poisson process is replaced with some non-negative integrable function of <math>t</math>, the resulting process is called an inhomogeneous or nonhomogeneous Poisson process, where the average density of points of the process is no longer constant. Serving as a fundamental process in queueing theory, the Poisson process is an important process for mathematical models, where it finds applications for models of events randomly occurring in certain time windows.

The Poisson process is a stochastic process that has different forms and definitions. It can be defined as a counting process, which is a stochastic process that represents the random number of points or events up to some time. The number of points of the process that are located in the interval from zero to some given time is a Poisson random variable that depends on that time and some parameter. This process has the natural numbers as its state space and the non-negative numbers as its index set. This process is also called the Poisson counting process, since it can be interpreted as an example of a counting process. The homogeneous Poisson process is a member of important classes of stochastic processes such as Markov processes and Lévy processes. If the parameter constant of the Poisson process is replaced with some non-negative integrable function of <math>t</math>, the resulting process is called an inhomogeneous or nonhomogeneous Poisson process, where the average density of points of the process is no longer constant. Serving as a fundamental process in queueing theory, the Poisson process is an important process for mathematical models, where it finds applications for models of events randomly occurring in certain time windows.

泊松过程是一个具有不同形式和定义的随机过程过程。它可以被定义为一个计数过程，这是一个随机过程，代表点或事件的随机数到一定时间。从零到给定时间区间内的过程点数是泊松随机变量，取决于该时间和某些参数。该过程以自然数为状态空间，非负数为索引集。这个过程也被称为泊松计数过程，因为它可以被解释为计数过程的一个例子。齐次泊松过程是一类重要的随机过程，如马尔可夫过程和 Lévy 过程的成员。如果将泊松过程的参数常数替换为 < math > t </math > 的非负可积函数，则得到的过程称为非齐次或非齐次泊松过程，其点的平均密度不再是常数。泊松过程作为排队论中的一个基本过程，是数学模型中的一个重要过程，它在特定时间窗内随机发生的事件模型中找到了应用。

'''<font color="#ff8000"> 泊松过程Poisson process</font>'''是一个具有不同形式和定义的随机过程。它可以被定义为一个计数过程，这是一个随机过程，代表到某个时间，点或事件的随机数。从零到给定时间区间内的过程点数是泊松随机变量，取决于该时间和某些参数。该过程以自然数为状态空间，非负数为索引集。这个过程也被称为泊松计数过程，因为它可以被解释为计数过程的一个例子。'''<font color="#ff8000"> 齐次泊松过程Homogeneous Poisson process</font>'''是一类重要的随机过程，如马尔可夫过程和 Lévy 过程的成员。如果将泊松过程的参数常数替换为 < math > t </math > 的非负可积函数，则得到的过程称为'''<font color="#ff8000"> 非齐次或非齐次泊松过程Inhomogeneous or nonhomogeneous Poisson process</font>'''，其点的平均密度不再是常数。泊松过程作为排队论中的一个基本过程，是数学模型中的一个重要过程，它在特定时间窗内随机发生的事件模型中找到了应用。

< 中心 > < 数学 >

< 中心 > < 数学 >

===Wiener process===

==='''<font color="#ff8000"> Wiener process维纳过程</font>'''===

\{X(t):t\in T \}.

\{X(t):t\in T \}.

The Wiener process is a stochastic process with stationary and [[independent increments]] that are [[normally distributed]] based on the size of the increments.<ref name="RogersWilliams2000page1">{{cite book|author1=L. C. G. Rogers|author2=David Williams|title=Diffusions, Markov Processes, and Martingales: Volume 1, Foundations|url=https://books.google.com/books?id=W0ydAgAAQBAJ&pg=PA1|year=2000|publisher=Cambridge University Press|isbn=978-1-107-71749-7|page=1}}</ref><ref name="Klebaner2005page56">{{cite book|author=Fima C. Klebaner|title=Introduction to Stochastic Calculus with Applications|url=https://books.google.com/books?id=JYzW0uqQxB0C|year=2005|publisher=Imperial College Press|isbn=978-1-86094-555-7|page=56}}</ref> The Wiener process is named after [[Norbert Wiener]], who proved its mathematical existence, but the process is also called the Brownian motion process or just Brownian motion due to its historical connection as a model for [[Brownian movement]] in liquids.<ref name="Brush1968page1">{{cite journal|last1=Brush|first1=Stephen G.|title=A history of random processes|journal=Archive for History of Exact Sciences|volume=5|issue=1|year=1968|pages=1–2|issn=0003-9519|doi=10.1007/BF00328110}}</ref><ref name="Applebaum2004page1338">{{cite journal|last1=Applebaum|first1=David|title=Lévy processes: From probability to finance and quantum groups|journal=Notices of the AMS|volume=51|issue=11|year=2004|pages=1338}}</ref><ref name="Applebaum2004page1338"/><ref name="GikhmanSkorokhod1969page21">{{cite book|author1=Iosif Ilyich Gikhman|author2=Anatoly Vladimirovich Skorokhod|title=Introduction to the Theory of Random Processes|url=https://books.google.com/books?id=yJyLzG7N7r8C&pg=PR2|year=1969|publisher=Courier Corporation|isbn=978-0-486-69387-3|page=21}}</ref>

The Wiener process is a stochastic process with stationary and [[independent increments]] that are [[normally distributed]] based on the size of the increments.<ref name="RogersWilliams2000page1">{{cite book|author1=L. C. G. Rogers|author2=David Williams|title=Diffusions, Markov Processes, and Martingales: Volume 1, Foundations|url=https://books.google.com/books?id=W0ydAgAAQBAJ&pg=PA1|year=2000|publisher=Cambridge University Press|isbn=978-1-107-71749-7|page=1}}</ref><ref name="Klebaner2005page56">{{cite book|author=Fima C. Klebaner|title=Introduction to Stochastic Calculus with Applications|url=https://books.google.com/books?id=JYzW0uqQxB0C|year=2005|publisher=Imperial College Press|isbn=978-1-86094-555-7|page=56}}</ref> The Wiener process is named after [[Norbert Wiener]], who proved its mathematical existence, but the process is also called the Brownian motion process or just Brownian motion due to its historical connection as a model for [[Brownian movement]] in liquids.<ref name="Brush1968page1">{{cite journal|last1=Brush|first1=Stephen G.|title=A history of random processes|journal=Archive for History of Exact Sciences|volume=5|issue=1|year=1968|pages=1–2|issn=0003-9519|doi=10.1007/BF00328110}}</ref><ref name="Applebaum2004page1338">{{cite journal|last1=Applebaum|first1=David|title=Lévy processes: From probability to finance and quantum groups|journal=Notices of the AMS|volume=51|issue=11|year=2004|pages=1338}}</ref><ref name="Applebaum2004page1338"/><ref name="GikhmanSkorokhod1969page21">{{cite book|author1=Iosif Ilyich Gikhman|author2=Anatoly Vladimirovich Skorokhod|title=Introduction to the Theory of Random Processes|url=https://books.google.com/books?id=yJyLzG7N7r8C&pg=PR2|year=1969|publisher=Courier Corporation|isbn=978-0-486-69387-3|page=21}}</ref>

Wiener过程是一个随机过程，具有平稳的[[独立的增量]]并且基于增量的大小是[[正态分布的].<ref name=“RogersWilliams2000page1”>{cite book | author1=L.C.G.Rogers | author2=David Williams | title=扩散、马尔可夫过程和鞅：第1卷，基金会网址=https://books.google.com/books？id=W0ydAgAAQBAJ&pg=PA1 | year=2000 | publisher=Cambridge University Press | isbn=978-1-107-71749-7 | page=1}</ref><ref name=“Klebaner2005page56”>{cite book | author=Fima C.Klebaner | title=随机微积分及其应用简介|网址=https://books.google.com/books？id=JYzW0uqQxB0C | year=2005 | publisher=Imperial College Press | isbn=978-1-86094-555-7 | page=56}</ref>维纳过程是以[[Norbert Wiener]]命名的，他证明了它的数学存在性，但是这个过程也被称为布朗运动过程或仅仅是布朗运动，因为它是液体中[[布朗运动]]的模型科学|卷=5 |议题=1 |年份=1968 |页数=1-2 | issn=0003-9519 | doi=10.1007/BF00328110}}</ref><ref name=“applebauma2004page1338”{{{引用杂志| last1=Applebaum | first1=David | title=Lévy过程：从概率到金融和量子群的概率到金融和量子群| journal=Na从概率到金融和量子群| journal=通知AMS | volume=51 | volume=11；年份=2004 |页数=1338}</ref><refname=“Applebaum2004page1338”/><ref name=“GikhmanSkorokhod1969page21”>{cite book | author1=Iosif Ilyich Gikhman | author2=Anatoly Vladimirovich skorokod | title=随机过程理论简介| url=https://books.google.com/books？id=yJyLzG7N7r8C&pg=PR2 |年份=1969 | publisher=Courier Corporation | isbn=978-0-486-69387-3 | page=21}</ref>

'''<font color="#ff8000"> Wiener process维纳过程</font>'''是一个随机过程，具有平稳的[[独立的增量]]并且基于增量的大小是[[正态分布的].<ref name=“RogersWilliams2000page1”>{cite book | author1=L.C.G.Rogers | author2=David Williams | title=扩散、马尔可夫过程和鞅：第1卷，基金会网址=https://books.google.com/books？id=W0ydAgAAQBAJ&pg=PA1 | year=2000 | publisher=Cambridge University Press | isbn=978-1-107-71749-7 | page=1}</ref><ref name=“Klebaner2005page56”>{cite book | author=Fima C.Klebaner | title=随机微积分及其应用简介|网址=https://books.google.com/books？id=JYzW0uqQxB0C | year=2005 | publisher=Imperial College Press | isbn=978-1-86094-555-7 | page=56}</ref>维纳过程是以[[Norbert Wiener]]命名的，他证明了它的数学存在性，但是这个过程也被称为布朗运动过程或仅仅是布朗运动，因为它是液体中[[布朗运动]]的模型科学|卷=5 |议题=1 |年份=1968 |页数=1-2 | issn=0003-9519 | doi=10.1007/BF00328110}}</ref><ref name=“applebauma2004page1338”{{{引用杂志| last1=Applebaum | first1=David | title=Lévy过程：从概率到金融和量子群的概率到金融和量子群| journal=Na从概率到金融和量子群| journal=通知AMS | volume=51 | volume=11；年份=2004 |页数=1338}</ref><refname=“Applebaum2004page1338”/><ref name=“GikhmanSkorokhod1969page21”>{cite book | author1=Iosif Ilyich Gikhman | author2=Anatoly Vladimirovich skorokod | title=随机过程理论简介| url=https://books.google.com/books？id=yJyLzG7N7r8C&pg=PR2 |年份=1969 | publisher=Courier Corporation | isbn=978-0-486-69387-3 | page=21}</ref>

Historically, in many problems from the natural sciences a point <math>t\in T</math> had the meaning of time, so <math>X(t)</math> is a random variable representing a value observed at time <math>t</math>. A stochastic process can also be written as <math> \{X(t,\omega):t\in T \}</math> to reflect that it is actually a function of two variables, <math>t\in T</math> and <math>\omega\in \Omega</math>.

Historically, in many problems from the natural sciences a point <math>t\in T</math> had the meaning of time, so <math>X(t)</math> is a random variable representing a value observed at time <math>t</math>. A stochastic process can also be written as <math> \{X(t,\omega):t\in T \}</math> to reflect that it is actually a function of two variables, <math>t\in T</math> and <math>\omega\in \Omega</math>.

Playing a central role in the theory of probability, the Wiener process is often considered the most important and studied stochastic process, with connections to other stochastic processes.<ref name="doob1953stochasticP46to47"/><ref name="RogersWilliams2000page1"/><ref name="Steele2012page29">{{cite book|author=J. Michael Steele|title=Stochastic Calculus and Financial Applications|url=https://books.google.com/books?id=fsgkBAAAQBAJ&pg=PR4|year=2012|publisher=Springer Science & Business Media|isbn=978-1-4684-9305-4|page=29}}</ref><ref name="Florescu2014page471">{{cite book|author=Ionut Florescu|title=Probability and Stochastic Processes|url=https://books.google.com/books?id=Z5xEBQAAQBAJ&pg=PR22|year=2014|publisher=John Wiley & Sons|isbn=978-1-118-59320-2|page=471}}</ref><ref name="KarlinTaylor2012page21">{{cite book|author1=Samuel Karlin|author2=Howard E. Taylor|title=A First Course in Stochastic Processes|url=https://books.google.com/books?id=dSDxjX9nmmMC|year=2012|publisher=Academic Press|isbn=978-0-08-057041-9|pages=21, 22}}</ref><ref name="KaratzasShreve2014pageVIII">{{cite book|author1=Ioannis Karatzas|author2=Steven Shreve|title=Brownian Motion and Stochastic Calculus|url=https://books.google.com/books?id=w0SgBQAAQBAJ&pg=PT5|year=1991|publisher=Springer|isbn=978-1-4612-0949-2|page=VIII}}</ref><ref name="RevuzYor2013pageIX">{{cite book|author1=Daniel Revuz|author2=Marc Yor|title=Continuous Martingales and Brownian Motion|url=https://books.google.com/books?id=OYbnCAAAQBAJ|year=2013|publisher=Springer Science & Business Media|isbn=978-3-662-06400-9|page=IX|author1-link=Daniel Revuz}}</ref> Its index set and state space are the non-negative numbers and real numbers, respectively, so it has both continuous index set and states space.<ref name="Rosenthal2006page186">{{cite book|author=Jeffrey S Rosenthal|title=A First Look at Rigorous Probability Theory|url=https://books.google.com/books?id=am1IDQAAQBAJ|year=2006|publisher=World Scientific Publishing Co Inc|isbn=978-981-310-165-4|page=186}}</ref> But the process can be defined more generally so its state space can be <math>n</math>-dimensional Euclidean space.<ref name="Klebaner2005page81"/><ref name="KarlinTaylor2012page21"/><ref>{{cite book|author1=Donald L. Snyder|author2=Michael I. Miller|title=Random Point Processes in Time and Space|url=https://books.google.com/books?id=c_3UBwAAQBAJ|year=2012|publisher=Springer Science & Business Media|isbn=978-1-4612-3166-0|page=33}}</ref> If the [[mean]] of any increment is zero, then the resulting Wiener or Brownian motion process is said to have zero drift. If the mean of the increment for any two points in time is equal to the time difference multiplied by some constant <math> \mu</math>, which is a real number, then the resulting stochastic process is said to have drift <math> \mu</math>.<ref name="Steele2012page118">{{cite book|author=J. Michael Steele|title=Stochastic Calculus and Financial Applications|url=https://books.google.com/books?id=fsgkBAAAQBAJ&pg=PR4|year=2012|publisher=Springer Science & Business Media|isbn=978-1-4684-9305-4|page=118}}</ref><ref name="MörtersPeres2010page1"/><ref name="KaratzasShreve2014page78">{{cite book|author1=Ioannis Karatzas|author2=Steven Shreve|title=Brownian Motion and Stochastic Calculus|url=https://books.google.com/books?id=w0SgBQAAQBAJ&pg=PT5|year=1991|publisher=Springer|isbn=978-1-4612-0949-2|page=78}}</ref>

Playing a central role in the theory of probability, the Wiener process is often considered the most important and studied stochastic process, with connections to other stochastic processes.<ref name="doob1953stochasticP46to47"/><ref name="RogersWilliams2000page1"/><ref name="Steele2012page29">{{cite book|author=J. Michael Steele|title=Stochastic Calculus and Financial Applications|url=https://books.google.com/books?id=fsgkBAAAQBAJ&pg=PR4|year=2012|publisher=Springer Science & Business Media|isbn=978-1-4684-9305-4|page=29}}</ref><ref name="Florescu2014page471">{{cite book|author=Ionut Florescu|title=Probability and Stochastic Processes|url=https://books.google.com/books?id=Z5xEBQAAQBAJ&pg=PR22|year=2014|publisher=John Wiley & Sons|isbn=978-1-118-59320-2|page=471}}</ref><ref name="KarlinTaylor2012page21">{{cite book|author1=Samuel Karlin|author2=Howard E. Taylor|title=A First Course in Stochastic Processes|url=https://books.google.com/books?id=dSDxjX9nmmMC|year=2012|publisher=Academic Press|isbn=978-0-08-057041-9|pages=21, 22}}</ref><ref name="KaratzasShreve2014pageVIII">{{cite book|author1=Ioannis Karatzas|author2=Steven Shreve|title=Brownian Motion and Stochastic Calculus|url=https://books.google.com/books?id=w0SgBQAAQBAJ&pg=PT5|year=1991|publisher=Springer|isbn=978-1-4612-0949-2|page=VIII}}</ref><ref name="RevuzYor2013pageIX">{{cite book|author1=Daniel Revuz|author2=Marc Yor|title=Continuous Martingales and Brownian Motion|url=https://books.google.com/books?id=OYbnCAAAQBAJ|year=2013|publisher=Springer Science & Business Media|isbn=978-3-662-06400-9|page=IX|author1-link=Daniel Revuz}}</ref> Its index set and state space are the non-negative numbers and real numbers, respectively, so it has both continuous index set and states space.<ref name="Rosenthal2006page186">{{cite book|author=Jeffrey S Rosenthal|title=A First Look at Rigorous Probability Theory|url=https://books.google.com/books?id=am1IDQAAQBAJ|year=2006|publisher=World Scientific Publishing Co Inc|isbn=978-981-310-165-4|page=186}}</ref> But the process can be defined more generally so its state space can be <math>n</math>-dimensional Euclidean space.<ref name="Klebaner2005page81"/><ref name="KarlinTaylor2012page21"/><ref>{{cite book|author1=Donald L. Snyder|author2=Michael I. Miller|title=Random Point Processes in Time and Space|url=https://books.google.com/books?id=c_3UBwAAQBAJ|year=2012|publisher=Springer Science & Business Media|isbn=978-1-4612-3166-0|page=33}}</ref> If the [[mean]] of any increment is zero, then the resulting Wiener or Brownian motion process is said to have zero drift. If the mean of the increment for any two points in time is equal to the time difference multiplied by some constant <math> \mu</math>, which is a real number, then the resulting stochastic process is said to have drift <math> \mu</math>.<ref name="Steele2012page118">{{cite book|author=J. Michael Steele|title=Stochastic Calculus and Financial Applications|url=https://books.google.com/books?id=fsgkBAAAQBAJ&pg=PR4|year=2012|publisher=Springer Science & Business Media|isbn=978-1-4684-9305-4|page=118}}</ref><ref name="MörtersPeres2010page1"/><ref name="KaratzasShreve2014page78">{{cite book|author1=Ioannis Karatzas|author2=Steven Shreve|title=Brownian Motion and Stochastic Calculus|url=https://books.google.com/books?id=w0SgBQAAQBAJ&pg=PT5|year=1991|publisher=Springer|isbn=978-1-4612-0949-2|page=78}}</ref>

Wiener过程在概率论中起着中心作用，通常被认为是最重要和研究的随机过程，并与其他随机过程联系在一起微积分与金融应用|网址=https://books.google.com/books？id=fsgkbaaqbaj&pg=PR4 | year=2012 | publisher=Springer Science&Business Media | isbn=978-1-4684-9305-4 | page=29}</ref><ref name=“florescu214page471”>{cite book |作者=Ionut Florescu | title=概率与随机过程|网址=https://books.google.com/books？id=Z5xEBQAAQBAJ&pg=PR22 | year=2014 | publisher=John Wiley&Sons | isbn=978-1-118-59320-2 | page=471}</ref><ref name=“KarlinTaylor2012page21”>{cite book | author1=Samuel Karlin | author2=Howard E.Taylor | title=随机过程的第一门课程| url=https://books.google.com/books？id=dSDxjX9nmmMC | year=2012 | publisher=academical Press | isbn=978-0-08-057041-9 | pages=21，22}</ref><ref name=“karatzarshreeve2014pageviii”{引用图书| author1=Ioannis Karatzas | author2=Steven Shreve | title=布朗运动和随机微积分| url=https://books.google.com/books？id=w0SgBQAAQBAJ&pg=PT5 | year=1991；publisher=Springer | isbn=978-1-4612-0949-2 | page=VIII}</ref><ref name=“RevuzYor2013pageIX”>{cite book | author1=Daniel Revuz | author2=Marc Yor| title=连续鞅和布朗运动| url=https://books.google.com/books？id=oybncaaqbaj | year=2013 | publisher=Springer Science&Business Media | isbn=978-3-662-06400-9 | page=IX | author1 link=Daniel Revuz}</ref>其索引集和状态空间分别是非负数和实数，因此它既有连续索引集又有状态空间=https://books.google.com/books？id=am1IDQAAQBAJ | year=2006 | publisher=World Scientific Publishing Co Inc | isbn=978-981-310-165-4 | page=186}</ref>但是过程可以定义得更广泛，这样它的状态空间可以是维欧几里德空间。<ref name=“klebaner205page81”/><ref name=“KarlinTaylor2012page21”/><ref>{cite book | author1=Donald L。Snyder | author2=Michael I.Miller | title=时空中的随机点过程| url=https://books.google.com/books？id=c_3UBwAAQBAJ | year=2012 | publisher=Springer Science&Business Media | isbn=978-1-4612-3166-0 | page=33}</ref>如果任何增量的[[平均值]]为零，则所得到的维纳或布朗运动过程称为零漂移。如果任意两个时间点的增量的平均值等于时间差乘以某个常数<math>\mu</math>，即实数，由此产生的随机过程被称为漂移<math>\mu</math><ref name=“Steele2012page118”>{cite book | author=J.Michael Steele | title=随机微积分和金融应用程序| url=https://books.google.com/books？id=fsgkbaaqbaj&pg=PR4 | year=2012 | publisher=Springer Science&Business Media | isbn=978-1-4684-9305-4 | page=118}</ref><ref name=“MörtersPeres2010page1”/><ref name=“Karatzasshreeve2014page78”>{cite book | author1=Ioannis Karatzas | author2=Steven Shreve | title=布朗运动和随机演算| url=https://books.google.com/books？id=w0SgBQAAQBAJ&pg=PT5 |年份=1991 | publisher=Springer | isbn=978-1-4612-0949-2 | page=78}</ref>

'''<font color="#ff8000"> Wiener process维纳过程</font>'''在概率论中起着中心作用，通常被认为是最重要和研究的随机过程，并与其他随机过程联系在一起微积分与金融应用|网址=https://books.google.com/books？id=fsgkbaaqbaj&pg=PR4 | year=2012 | publisher=Springer Science&Business Media | isbn=978-1-4684-9305-4 | page=29}</ref><ref name=“florescu214page471”>{cite book |作者=Ionut Florescu | title=概率与随机过程|网址=https://books.google.com/books？id=Z5xEBQAAQBAJ&pg=PR22 | year=2014 | publisher=John Wiley&Sons | isbn=978-1-118-59320-2 | page=471}</ref><ref name=“KarlinTaylor2012page21”>{cite book | author1=Samuel Karlin | author2=Howard E.Taylor | title=随机过程的第一门课程| url=https://books.google.com/books？id=dSDxjX9nmmMC | year=2012 | publisher=academical Press | isbn=978-0-08-057041-9 | pages=21，22}</ref><ref name=“karatzarshreeve2014pageviii”{引用图书| author1=Ioannis Karatzas | author2=Steven Shreve | title=布朗运动和随机微积分| url=https://books.google.com/books？id=w0SgBQAAQBAJ&pg=PT5 | year=1991；publisher=Springer | isbn=978-1-4612-0949-2 | page=VIII}</ref><ref name=“RevuzYor2013pageIX”>{cite book | author1=Daniel Revuz | author2=Marc Yor| title=连续鞅和布朗运动| url=https://books.google.com/books？id=oybncaaqbaj | year=2013 | publisher=Springer Science&Business Media | isbn=978-3-662-06400-9 | page=IX | author1 link=Daniel Revuz}</ref>其索引集和状态空间分别是非负数和实数，因此它既有连续索引集又有状态空间=https://books.google.com/books？id=am1IDQAAQBAJ | year=2006 | publisher=World Scientific Publishing Co Inc | isbn=978-981-310-165-4 | page=186}</ref>但是过程可以定义得更广泛，这样它的状态空间可以是维欧几里德空间。<ref name=“klebaner205page81”/><ref name=“KarlinTaylor2012page21”/><ref>{cite book | author1=Donald L。Snyder | author2=Michael I.Miller | title=时空中的随机点过程| url=https://books.google.com/books？id=c_3UBwAAQBAJ | year=2012 | publisher=Springer Science&Business Media | isbn=978-1-4612-3166-0 | page=33}</ref>如果任何增量的[[平均值]]为零，则所得到的维纳或布朗运动过程称为零漂移。如果任意两个时间点的增量的平均值等于时间差乘以某个常数<math>\mu</math>，即实数，由此产生的随机过程被称为漂移<math>\mu</math><ref name=“Steele2012page118”>{cite book | author=J.Michael Steele | title=随机微积分和金融应用程序| url=https://books.google.com/books？id=fsgkbaaqbaj&pg=PR4 | year=2012 | publisher=Springer Science&Business Media | isbn=978-1-4684-9305-4 | page=118}</ref><ref name=“MörtersPeres2010page1”/><ref name=“Karatzasshreeve2014page78”>{cite book | author1=Ioannis Karatzas | author2=Steven Shreve | title=布朗运动和随机演算| url=https://books.google.com/books？id=w0SgBQAAQBAJ&pg=PT5 |年份=1991 | publisher=Springer | isbn=978-1-4612-0949-2 | page=78}</ref>

The mathematical space <math>S</math> of a stochastic process is called its state space. This mathematical space can be defined using integers, real lines, <math>n</math>-dimensional Euclidean spaces, complex planes, or more abstract mathematical spaces. The state space is defined using elements that reflect the different values that the stochastic process can take.

The mathematical space <math>S</math> of a stochastic process is called its state space. This mathematical space can be defined using integers, real lines, <math>n</math>-dimensional Euclidean spaces, complex planes, or more abstract mathematical spaces. The state space is defined using elements that reflect the different values that the stochastic process can take.

[[Almost surely]], a sample path of a Wiener process is continuous everywhere but [[nowhere differentiable function|nowhere differentiable]]. It can be considered as a continuous version of the simple random walk.<ref name="Applebaum2004page1337">{{cite journal|last1=Applebaum|first1=David|title=Lévy processes: From probability to finance and quantum groups|journal=Notices of the AMS|volume=51|issue=11|year=2004|page=1337}}</ref><ref name="MörtersPeres2010page1">{{cite book|author1=Peter Mörters|author2=Yuval Peres|title=Brownian Motion|url=https://books.google.com/books?id=e-TbA-dSrzYC|year=2010|publisher=Cambridge University Press|isbn=978-1-139-48657-6|pages=1, 3}}</ref> The process arises as the mathematical limit of other stochastic processes such as certain random walks rescaled,<ref name="KaratzasShreve2014page61">{{cite book|author1=Ioannis Karatzas|author2=Steven Shreve|title=Brownian Motion and Stochastic Calculus|url=https://books.google.com/books?id=w0SgBQAAQBAJ&pg=PT5|year=1991|publisher=Springer|isbn=978-1-4612-0949-2|page=61}}</ref><ref name="Shreve2004page93">{{cite book|author=Steven E. Shreve|title=Stochastic Calculus for Finance II: Continuous-Time Models|url=https://books.google.com/books?id=O8kD1NwQBsQC|year=2004|publisher=Springer Science & Business Media|isbn=978-0-387-40101-0|page=93}}</ref> which is the subject of [[Donsker's theorem]] or invariance principle, also known as the functional central limit theorem.<ref name="Kallenberg2002page225and260">{{cite book|author=Olav Kallenberg|title=Foundations of Modern Probability|url=https://books.google.com/books?id=L6fhXh13OyMC|year=2002|publisher=Springer Science & Business Media|isbn=978-0-387-95313-7|pages=225, 260}}</ref><ref name="KaratzasShreve2014page70">{{cite book|author1=Ioannis Karatzas|author2=Steven Shreve|title=Brownian Motion and Stochastic Calculus|url=https://books.google.com/books?id=w0SgBQAAQBAJ&pg=PT5|year=1991|publisher=Springer|isbn=978-1-4612-0949-2|page=70}}</ref><ref name="MörtersPeres2010page131">{{cite book|author1=Peter Mörters|author2=Yuval Peres|title=Brownian Motion|url=https://books.google.com/books?id=e-TbA-dSrzYC|year=2010|publisher=Cambridge University Press|isbn=978-1-139-48657-6|page=131}}</ref>

[[Almost surely]], a sample path of a Wiener process is continuous everywhere but [[nowhere differentiable function|nowhere differentiable]]. It can be considered as a continuous version of the simple random walk.<ref name="Applebaum2004page1337">{{cite journal|last1=Applebaum|first1=David|title=Lévy processes: From probability to finance and quantum groups|journal=Notices of the AMS|volume=51|issue=11|year=2004|page=1337}}</ref><ref name="MörtersPeres2010page1">{{cite book|author1=Peter Mörters|author2=Yuval Peres|title=Brownian Motion|url=https://books.google.com/books?id=e-TbA-dSrzYC|year=2010|publisher=Cambridge University Press|isbn=978-1-139-48657-6|pages=1, 3}}</ref> The process arises as the mathematical limit of other stochastic processes such as certain random walks rescaled,<ref name="KaratzasShreve2014page61">{{cite book|author1=Ioannis Karatzas|author2=Steven Shreve|title=Brownian Motion and Stochastic Calculus|url=https://books.google.com/books?id=w0SgBQAAQBAJ&pg=PT5|year=1991|publisher=Springer|isbn=978-1-4612-0949-2|page=61}}</ref><ref name="Shreve2004page93">{{cite book|author=Steven E. Shreve|title=Stochastic Calculus for Finance II: Continuous-Time Models|url=https://books.google.com/books?id=O8kD1NwQBsQC|year=2004|publisher=Springer Science & Business Media|isbn=978-0-387-40101-0|page=93}}</ref> which is the subject of [[Donsker's theorem]] or invariance principle, also known as the functional central limit theorem.<ref name="Kallenberg2002page225and260">{{cite book|author=Olav Kallenberg|title=Foundations of Modern Probability|url=https://books.google.com/books?id=L6fhXh13OyMC|year=2002|publisher=Springer Science & Business Media|isbn=978-0-387-95313-7|pages=225, 260}}</ref><ref name="KaratzasShreve2014page70">{{cite book|author1=Ioannis Karatzas|author2=Steven Shreve|title=Brownian Motion and Stochastic Calculus|url=https://books.google.com/books?id=w0SgBQAAQBAJ&pg=PT5|year=1991|publisher=Springer|isbn=978-1-4612-0949-2|page=70}}</ref><ref name="MörtersPeres2010page131">{{cite book|author1=Peter Mörters|author2=Yuval Peres|title=Brownian Motion|url=https://books.google.com/books?id=e-TbA-dSrzYC|year=2010|publisher=Cambridge University Press|isbn=978-1-139-48657-6|page=131}}</ref>

[[几乎可以肯定]]，Wiener过程的样本路径处处连续，但[[无处可微函数|无处可微]]。它可以看作是简单随机游走的一个连续版本。<ref name=“Applebaum2004page1337”>{cite journal | last1=Applebaum | first1=David | title=Lévy过程：从概率到金融和量子群| journal=AMS的通知| volume=51 | issue=11 | year=2004|page=1337}</ref name=“MörtersPeres2010page1”>{citebook | author1=Peter Mörters | author2=Yuval Peres | title=布朗运动|网址=https://books.google.com/books？id=e-TbA-dSrzYC | year=2010 | publisher=Cambridge University Press | isbn=978-1-139-48657-6 | pages=1，3}}</ref>当其他随机过程（如某些随机游动重新缩放）的数学极限时，该过程出现，<ref name=“KaratzasShreve2014page61”>{cite book | author1=Ioannis Karatzas | author2=Steven Shreve | title=布朗运动和随机微积分| url=https://books.google.com/books？id=w0SgBQAAQBAJ&pg=PT5 | year=1991 | publisher=Springer | isbn=978-1-4612-0949-2 | page=61}</ref><ref name=“Shreve2004page93”>{cite book |作者=Steven E.Shreve | title=金融随机微积分II：连续时间模型| url=https://books.google.com/books？id=O8kD1NwQBsQC | year=2004 | publisher=Springer Science&Business Media | isbn=978-0-387-40101-0 | page=93}</ref>这是[[Donsker定理]]或不变性原理的主题，也被称为函数中心极限定理=https://books.google.com/books？id=L6fhXh13OyMC | year=2002 | publisher=Springer Science&Business Media | isbn=978-0-387-95313-7 | pages=225260}}</ref><ref name=“karatzarshreve2014page70”{引用图书| author1=Ioannis Karatzas | author2=Steven Shreve | title=布朗运动和随机演算| url=https://books.google.com/books？id=w0SgBQAAQBAJ&pg=PT5|year=1991；publisher=Springer | isbn=978-1-4612-0949-2 | page=70}</ref><ref name=“MörtersPeres2010page131”>{cite book | author1=Peter Mörters | author2=Yuval Peres | title=布朗运动| url=https://books.google.com/books？id=e-TbA-dSrzYC |年=2010 | publisher=剑桥大学出版社| isbn=978-1-139-48657-6 | page=131}</ref>

[[几乎可以肯定]]，'''<font color="#ff8000"> Wiener process维纳过程</font>'''的样本路径处处连续，但[[无处可微函数|无处可微]]。它可以看作是简单随机游走的一个连续版本。<ref name=“Applebaum2004page1337”>{cite journal | last1=Applebaum | first1=David | title=Lévy过程：从概率到金融和量子群| journal=AMS的通知| volume=51 | issue=11 | year=2004|page=1337}</ref name=“MörtersPeres2010page1”>{citebook | author1=Peter Mörters | author2=Yuval Peres | title=布朗运动|网址=https://books.google.com/books？id=e-TbA-dSrzYC | year=2010 | publisher=Cambridge University Press | isbn=978-1-139-48657-6 | pages=1，3}}</ref>当其他随机过程（如某些随机游动重新缩放）的数学极限时，该过程出现，<ref name=“KaratzasShreve2014page61”>{cite book | author1=Ioannis Karatzas | author2=Steven Shreve | title=布朗运动和随机微积分| url=https://books.google.com/books？id=w0SgBQAAQBAJ&pg=PT5 | year=1991 | publisher=Springer | isbn=978-1-4612-0949-2 | page=61}</ref><ref name=“Shreve2004page93”>{cite book |作者=Steven E.Shreve | title=金融随机微积分II：连续时间模型| url=https://books.google.com/books？id=O8kD1NwQBsQC | year=2004 | publisher=Springer Science&Business Media | isbn=978-0-387-40101-0 | page=93}</ref>这是[[Donsker定理]]或不变性原理的主题，也被称为函数中心极限定理=https://books.google.com/books？id=L6fhXh13OyMC | year=2002 | publisher=Springer Science&Business Media | isbn=978-0-387-95313-7 | pages=225260}}</ref><ref name=“karatzarshreve2014page70”{引用图书| author1=Ioannis Karatzas | author2=Steven Shreve | title=布朗运动和随机演算| url=https://books.google.com/books？id=w0SgBQAAQBAJ&pg=PT5|year=1991；publisher=Springer | isbn=978-1-4612-0949-2 | page=70}</ref><ref name=“MörtersPeres2010page131”>{cite book | author1=Peter Mörters | author2=Yuval Peres | title=布朗运动| url=https://books.google.com/books？id=e-TbA-dSrzYC |年=2010 | publisher=剑桥大学出版社| isbn=978-1-139-48657-6 | page=131}</ref>

The Wiener process is a member of some important families of stochastic processes, including Markov processes, Lévy processes and Gaussian processes.<ref name="RogersWilliams2000page1"/><ref name="Applebaum2004page1337"/> The process also has many applications and is the main stochastic process used in stochastic calculus.<ref name="Klebaner2005">{{cite book|author=Fima C. Klebaner|title=Introduction to Stochastic Calculus with Applications|url=https://books.google.com/books?id=JYzW0uqQxB0C|year=2005|publisher=Imperial College Press|isbn=978-1-86094-555-7}}</ref><ref name="KaratzasShreve2014page">{{cite book|author1=Ioannis Karatzas|author2=Steven Shreve|title=Brownian Motion and Stochastic Calculus|url=https://books.google.com/books?id=w0SgBQAAQBAJ&pg=PT5|year=1991|publisher=Springer|isbn=978-1-4612-0949-2}}</ref> It plays a central role in quantitative finance,<ref name="Applebaum2004page1341">{{cite journal|last1=Applebaum|first1=David|title=Lévy processes: From probability to finance and quantum groups|journal=Notices of the AMS|volume=51|issue=11|year=2004|page=1341}}</ref><ref name="KarlinTaylor2012page340">{{cite book|author1=Samuel Karlin|author2=Howard E. Taylor|title=A First Course in Stochastic Processes|url=https://books.google.com/books?id=dSDxjX9nmmMC|year=2012|publisher=Academic Press|isbn=978-0-08-057041-9|page=340}}</ref> where it is used, for example, in the Black–Scholes–Merton model.<ref name="Klebaner2005page124">{{cite book|author=Fima C. Klebaner|title=Introduction to Stochastic Calculus with Applications|url=https://books.google.com/books?id=JYzW0uqQxB0C|year=2005|publisher=Imperial College Press|isbn=978-1-86094-555-7|page=124}}</ref> The process is also used in different fields, including the majority of natural sciences as well as some branches of social sciences, as a mathematical model for various random phenomena.<ref name="Steele2012page29"/><ref name="KaratzasShreve2014page47">{{cite book|author1=Ioannis Karatzas|author2=Steven Shreve|title=Brownian Motion and Stochastic Calculus|url=https://books.google.com/books?id=w0SgBQAAQBAJ&pg=PT5|year=1991|publisher=Springer|isbn=978-1-4612-0949-2|page=47}}</ref><ref name="Wiersema2008page2">{{cite book|author=Ubbo F. Wiersema|title=Brownian Motion Calculus|url=https://books.google.com/books?id=0h-n0WWuD9cC|year=2008|publisher=John Wiley & Sons|isbn=978-0-470-02171-2|page=2}}</ref>

The Wiener process is a member of some important families of stochastic processes, including Markov processes, Lévy processes and Gaussian processes.<ref name="RogersWilliams2000page1"/><ref name="Applebaum2004page1337"/> The process also has many applications and is the main stochastic process used in stochastic calculus.<ref name="Klebaner2005">{{cite book|author=Fima C. Klebaner|title=Introduction to Stochastic Calculus with Applications|url=https://books.google.com/books?id=JYzW0uqQxB0C|year=2005|publisher=Imperial College Press|isbn=978-1-86094-555-7}}</ref><ref name="KaratzasShreve2014page">{{cite book|author1=Ioannis Karatzas|author2=Steven Shreve|title=Brownian Motion and Stochastic Calculus|url=https://books.google.com/books?id=w0SgBQAAQBAJ&pg=PT5|year=1991|publisher=Springer|isbn=978-1-4612-0949-2}}</ref> It plays a central role in quantitative finance,<ref name="Applebaum2004page1341">{{cite journal|last1=Applebaum|first1=David|title=Lévy processes: From probability to finance and quantum groups|journal=Notices of the AMS|volume=51|issue=11|year=2004|page=1341}}</ref><ref name="KarlinTaylor2012page340">{{cite book|author1=Samuel Karlin|author2=Howard E. Taylor|title=A First Course in Stochastic Processes|url=https://books.google.com/books?id=dSDxjX9nmmMC|year=2012|publisher=Academic Press|isbn=978-0-08-057041-9|page=340}}</ref> where it is used, for example, in the Black–Scholes–Merton model.<ref name="Klebaner2005page124">{{cite book|author=Fima C. Klebaner|title=Introduction to Stochastic Calculus with Applications|url=https://books.google.com/books?id=JYzW0uqQxB0C|year=2005|publisher=Imperial College Press|isbn=978-1-86094-555-7|page=124}}</ref> The process is also used in different fields, including the majority of natural sciences as well as some branches of social sciences, as a mathematical model for various random phenomena.<ref name="Steele2012page29"/><ref name="KaratzasShreve2014page47">{{cite book|author1=Ioannis Karatzas|author2=Steven Shreve|title=Brownian Motion and Stochastic Calculus|url=https://books.google.com/books?id=w0SgBQAAQBAJ&pg=PT5|year=1991|publisher=Springer|isbn=978-1-4612-0949-2|page=47}}</ref><ref name="Wiersema2008page2">{{cite book|author=Ubbo F. Wiersema|title=Brownian Motion Calculus|url=https://books.google.com/books?id=0h-n0WWuD9cC|year=2008|publisher=John Wiley & Sons|isbn=978-0-470-02171-2|page=2}}</ref>

维纳过程是一些重要的随机过程家族的成员，包括马尔可夫过程，Lévy过程和高斯过程。<ref name=“RogersWilliams2000page1”/><ref name=“Applebaum2004page1337”/>该过程也有许多应用，是随机微积分中使用的主要随机过程。<ref name=“Klebaner2005”>{cite book | author=Fima C.Klebaner | title=随机微积分简介应用程序| url=https://books.google.com/books？id=JYzW0uqQxB0C | year=2005 | publisher=Imperial College Press | isbn=978-1-86094-555-7}</ref><ref name=“KaratzasShreve2014page”>{引用图书| author1=Ioannis Karatzas | author2=Steven Shreve | title=布朗运动和随机微积分| url=https://books.google.com/books？id=w0SgBQAAQBAJ&pg=PT5 | year=1991 | publisher=Springer | isbn=978-1-4612-0949-2}</ref>它在数量金融中起着核心作用，{{本刊|从概率到金融金融和量子集团的过程〈124; journal从概率到金融和量子群的群| journal=journal=金融金融和量子群群| journal=noticof the AMS | volume=51 | issue=11 |年=2004年2004年| page=1341}}</ref><ref name=“KarlinTaylor2012page340 340{引用书〈author1=author1=Samuel Karlin | author1=Samuel Karlin；author2=Howard2=Howarde.Taylor | Howarde.Taylor标题=第一门课程随机过程| url=https://books.google.com/books？id=dSDxjX9nmmMC | year=2012 | publisher=academical Press | isbn=978-0-08-057041-9 | page=340}</ref>在Black-Scholes-Merton模型中使用它。<ref name=“Klebaner2005page124”>{cite book | author=Fima C.Klebaner | title=Introduction to Rastic Calculation with Applications |网址=https://books.google.com/books？id=JYzW0uqQxB0C | year=2005 | publisher=Imperial College Press | isbn=978-1-86094-555-7 | page=124}</ref>该过程也被用于不同的领域，包括大多数自然科学以及社会科学的一些分支，作为各种随机现象的数学模型=https://books.google.com/books？id=w0SgBQAAQBAJ&pg=PT5 | year=1991 | publisher=Springer | isbn=978-1-4612-0949-2 | page=47}</ref><ref name=“Wiersema2008page2”>{cite book |作者=Ubbo F.Wiersema | title=布朗运动演算| url=https://books.google.com/books？id=0h-n0WWuD9cC |年=2008 | publisher=John Wiley&Sons | isbn=978-0-470-02171-2 | page=2}</ref>

'''<font color="#ff8000"> Wiener process维纳过程</font>'''是一些重要的随机过程家族的成员，包括马尔可夫过程，Lévy过程和高斯过程。<ref name=“RogersWilliams2000page1”/><ref name=“Applebaum2004page1337”/>该过程也有许多应用，是随机微积分中使用的主要随机过程。<ref name=“Klebaner2005”>{cite book | author=Fima C.Klebaner | title=随机微积分简介应用程序| url=https://books.google.com/books？id=JYzW0uqQxB0C | year=2005 | publisher=Imperial College Press | isbn=978-1-86094-555-7}</ref><ref name=“KaratzasShreve2014page”>{引用图书| author1=Ioannis Karatzas | author2=Steven Shreve | title=布朗运动和随机微积分| url=https://books.google.com/books？id=w0SgBQAAQBAJ&pg=PT5 | year=1991 | publisher=Springer | isbn=978-1-4612-0949-2}</ref>它在数量金融中起着核心作用，{{本刊|从概率到金融金融和量子集团的过程〈124; journal从概率到金融和量子群的群| journal=journal=金融金融和量子群群| journal=noticof the AMS | volume=51 | issue=11 |年=2004年2004年| page=1341}}</ref><ref name=“KarlinTaylor2012page340 340{引用书〈author1=author1=Samuel Karlin | author1=Samuel Karlin；author2=Howard2=Howarde.Taylor | Howarde.Taylor标题=第一门课程随机过程| url=https://books.google.com/books？id=dSDxjX9nmmMC | year=2012 | publisher=academical Press | isbn=978-0-08-057041-9 | page=340}</ref>在Black-Scholes-Merton模型中使用它。<ref name=“Klebaner2005page124”>{cite book | author=Fima C.Klebaner | title=Introduction to Rastic Calculation with Applications |网址=https://books.google.com/books？id=JYzW0uqQxB0C | year=2005 | publisher=Imperial College Press | isbn=978-1-86094-555-7 | page=124}</ref>该过程也被用于不同的领域，包括大多数自然科学以及社会科学的一些分支，作为各种随机现象的数学模型=https://books.google.com/books？id=w0SgBQAAQBAJ&pg=PT5 | year=1991 | publisher=Springer | isbn=978-1-4612-0949-2 | page=47}</ref><ref name=“Wiersema2008page2”>{cite book |作者=Ubbo F.Wiersema | title=布朗运动演算| url=https://books.google.com/books？id=0h-n0WWuD9cC |年=2008 | publisher=John Wiley&Sons | isbn=978-0-470-02171-2 | page=2}</ref>

A sample function is a single outcome of a stochastic process, so it is formed by taking a single possible value of each random variable of the stochastic process. More precisely, if <math>\{X(t,\omega):t\in T \}</math> is a stochastic process, then for any point <math>\omega\in\Omega</math>, the mapping

A sample function is a single outcome of a stochastic process, so it is formed by taking a single possible value of each random variable of the stochastic process. More precisely, if <math>\{X(t,\omega):t\in T \}</math> is a stochastic process, then for any point <math>\omega\in\Omega</math>, the mapping

< 中心 > < 数学 >

< 中心 > < 数学 >

===Poisson process===

===Poisson process泊松过程===

X(\cdot,\omega): T \rightarrow S,

X(\cdot,\omega): T \rightarrow S,