370
个编辑
更改
跳到导航
跳到搜索
第643行：
第643行： − + − + − + − + − + − + − + − − The term Bayesian network was coined by Judea Pearl in 1985 to emphasize:<ref>{{cite conference |last=Pearl |first=J. | name-list-format = vanc |authorlink=Judea Pearl |year=1985 |title=Bayesian Networks: A Model of Self-Activated Memory for Evidential Reasoning |conference=Proceedings of the 7th Conference of the Cognitive Science Society, University of California, Irvine, CA − − 1985年，朱迪亚 · 珀尔创造了'''<font color="#ff8000"> 贝叶斯网络Bayesian network</font>'''一词来强调: ref { cite conference | last Pearl | first j。贝叶斯网络: 证据推理的自激记忆模型 | 第七届认知科学学会会议论文集，加州大学欧文分校 − − |pages=329–334 |url=http://ftp.cs.ucla.edu/tech-report/198_-reports/850017.pdf|access-date=2009-05-01 |format=UCLA Technical Report CSD-850017}}</ref> − − |pages=329–334 |url=http://ftp.cs.ucla.edu/tech-report/198_-reports/850017.pdf|access-date=2009-05-01 |format=UCLA Technical Report CSD-850017}}</ref> − − | 第329-334页 | 网址 / http://ftp.cs.UCLA.edu/tech-Report/198_-reports/850017.pdf|access-date=2009-05-01 / 格式 / UCLA 技术报告 / CSD-850017} / ref − + 第677行：
第667行： − + − 第684行：
第673行： − 20世纪80年代后期，皮尔的《智能系统中的概率推理》和那不勒斯的《专家系统中的概率推理》总结了它们的性质，并将它们确立为一个研究领域。+
→Software软件
* [[Just another Gibbs sampler]] (JAGS) – Open-source alternative to WinBUGS. Uses Gibbs sampling.
* [[Just another Gibbs sampler]] (JAGS) – Open-source alternative to WinBUGS. Uses Gibbs sampling.
* [[Just another Gibbs sampler]] (JAGS) WinBUGS的开源替代品。使用吉布斯抽样法
* [[Just another Gibbs sampler]] (JAGS) WinBUGS的开源替代品。使用了吉布斯采样。
* [[OpenBUGS]] – Open-source development of WinBUGS.
* [[OpenBUGS]] – Open-source development of WinBUGS.
* [[OpenBUGS]] –WinBUGS的开源开发。
* [[OpenBUGS]] –WinBUGS的开源版本。
* [[SPSS Modeler]] – Commercial software that includes an implementation for Bayesian networks.
* [[SPSS Modeler]] – Commercial software that includes an implementation for Bayesian networks.
* [[SPSS Modeler]] –包括贝叶斯网络实现的商业软件。
* [[SPSS Modeler]] –一个包括了贝叶斯网络实现的商业软件。
* [[Stan (software)]] – Stan is an open-source package for obtaining Bayesian inference using the No-U-Turn sampler (NUTS),<ref>{{Cite document |arxiv = 1111.4246|bibcode = 2011arXiv1111.4246H|last1 = Hoffman|first1 = Matthew D.|last2 = Gelman|first2 = Andrew|title = The No-U-Turn Sampler: Adaptively Setting Path Lengths in Hamiltonian Monte Carlo|year = 2011}}</ref> a variant of Hamiltonian Monte Carlo.
* [[Stan (software)]] – Stan is an open-source package for obtaining Bayesian inference using the No-U-Turn sampler (NUTS),<ref>{{Cite document |arxiv = 1111.4246|bibcode = 2011arXiv1111.4246H|last1 = Hoffman|first1 = Matthew D.|last2 = Gelman|first2 = Andrew|title = The No-U-Turn Sampler: Adaptively Setting Path Lengths in Hamiltonian Monte Carlo|year = 2011}}</ref> a variant of Hamiltonian Monte Carlo.
* [[Stan (software)]] – stan是一个开源软件包，用于使用不掉头取样器（NUTS），<ref>{{Cite document |arxiv = 1111.4246|bibcode = 2011arXiv1111.4246H|last1 = Hoffman|first1 = Matthew D.|last2 = Gelman|first2 = Andrew|title = The No-U-Turn Sampler:在哈密顿蒙特卡罗中自适应设置路径长度|年=2011}}的一个变体。
* [[Stan (software)]] – stan是一个开源软件包，用于使用No-U-Turn取样器（NUTS），<ref>{{Cite document |arxiv = 1111.4246|bibcode = 2011arXiv1111.4246H|last1 = Hoffman|first1 = Matthew D.|last2 = Gelman|first2 = Andrew|title = The No-U-Turn Sampler: Adaptively Setting Path Lengths in Hamiltonian Monte Carlo|year = 2011}}</ref> 是汉密尔顿蒙特卡洛方法的一个变体。
* [[PyMC3]] – A Python library implementing an embedded domain specific language to represent bayesian networks, and a variety of samplers (including NUTS)
* [[PyMC3]] – A Python library implementing an embedded domain specific language to represent bayesian networks, and a variety of samplers (including NUTS)
* [[PyMC3]] – 一个python库，它实现了一个嵌入式领域特定语言来表示贝叶斯网络和各种采样器（包括坚果NUTS）。
* [[PyMC3]] – 一个python库，它实现了一个能用来表示贝叶斯网络的微型语言，以及各种采样器（包括No-U-Turn取样器）。
* [[WinBUGS]] – One of the first computational implementations of MCMC samplers. No longer maintained.
* [[WinBUGS]] – One of the first computational implementations of MCMC samplers. No longer maintained.
* [[WinBUGS]] –MCMC采样器的第一个计算实现之一。不再支持。
* [[WinBUGS]] –马尔可夫链蒙特卡罗采样器最早的实现之一，但这个软件已经不再维护。
==History历史==
==History历史==
The term Bayesian network was coined by [[Judea Pearl]] in 1985 to emphasize:<ref>{{cite conference |last=Pearl |first=J. | name-list-format = vanc |authorlink=Judea Pearl |year=1985 |title=Bayesian Networks: A Model of Self-Activated Memory for Evidential Reasoning |conference=Proceedings of the 7th Conference of the Cognitive Science Society, University of California, Irvine, CA
The term Bayesian network was coined by [[Judea Pearl]] in 1985 to emphasize:
1985年，朱迪亚 · 珀尔创造了'''<font color="#ff8000">贝叶斯网络</font>'''一词来强调:
*the often subjective nature of the input information
*the often subjective nature of the input information
*依赖贝叶斯条件作为信息更新的基础
*依赖贝叶斯条件作为信息更新的基础
*the distinction between causal and evidential modes of reasoning<ref>{{Cite journal | last = Bayes | first = T. | name-list-format = vanc | authorlink = Thomas Bayes | year = 1763 | title = An Essay towards solving a Problem in the Doctrine of Chances | journal = [[Philosophical Transactions of the Royal Society]] | volume = 53 | pages = 370–418 | doi = 10.1098/rstl.1763.0053 | last2 = Price | title-link = An Essay towards solving a Problem in the Doctrine of Chances | doi-access = free }}</ref>
*the distinction between causal and evidential modes of reasoning<ref>{{Cite journal | last = Bayes | first = T. | name-list-format = vanc | authorlink = Thomas Bayes | year = 1763 | title = An Essay towards solving a Problem in the Doctrine of Chances | journal = [[Philosophical Transactions of the Royal Society]] | volume = 53 | pages = 370–418 | doi = 10.1098/rstl.1763.0053 | last2 = Price | title-link = An Essay towards solving a Problem in the Doctrine of Chances | doi-access = free }}</ref>
*因果推理和证据推理模式的区别<ref>{{Cite journal | last = Bayes | first = T. | name-list-format = vanc | authorlink = Thomas Bayes | year = 1763 | title = An Essay towards solving a Problem in the Doctrine of Chances | journal = [[Philosophical Transactions of the Royal Society]] | volume = 53 | pages = 370–418 | doi = 10.1098/rstl.1763.0053 | last2 = Price | title-link = An Essay towards solving a Problem in the Doctrine of Chances | doi-access = free }}</ref>
*因果推理和相关推理是有区别的
In the late 1980s Pearl's ''Probabilistic Reasoning in Intelligent Systems''<ref>{{cite book | vauthors = Pearl J |title=Probabilistic Reasoning in Intelligent Systems |publisher=[[Morgan Kaufmann]] |location=San Francisco CA |isbn=978-1558604797 |pages=1988 |url={{google books |plainurl=y |id=AvNID7LyMusC}}|date=1988-09-15 }}</ref> and [[Richard E. Neapolitan|Neapolitan]]'s ''Probabilistic Reasoning in Expert Systems''<ref>{{cite book |first=Richard E. |last=Neapolitan | name-list-format = vanc |title=Probabilistic reasoning in expert systems: theory and algorithms |url={{google books |plainurl=y |id=7X5KLwEACAAJ}} |year=1989 |publisher=Wiley |isbn=978-0-471-61840-9}}</ref> summarized their properties and established them as a field of study.
In the late 1980s Pearl's ''Probabilistic Reasoning in Intelligent Systems''<ref>{{cite book | vauthors = Pearl J |title=Probabilistic Reasoning in Intelligent Systems |publisher=[[Morgan Kaufmann]] |location=San Francisco CA |isbn=978-1558604797 |pages=1988 |url={{google books |plainurl=y |id=AvNID7LyMusC}}|date=1988-09-15 }}</ref> and [[Richard E. Neapolitan|Neapolitan]]'s ''Probabilistic Reasoning in Expert Systems''<ref>{{cite book |first=Richard E. |last=Neapolitan | name-list-format = vanc |title=Probabilistic reasoning in expert systems: theory and algorithms |url={{google books |plainurl=y |id=7X5KLwEACAAJ}} |year=1989 |publisher=Wiley |isbn=978-0-471-61840-9}}</ref> summarized their properties and established them as a field of study.
In the late 1980s Pearl's Probabilistic Reasoning in Intelligent Systems and Neapolitan's Probabilistic Reasoning in Expert Systems summarized their properties and established them as a field of study.
In the late 1980s Pearl's Probabilistic Reasoning in Intelligent Systems and Neapolitan's Probabilistic Reasoning in Expert Systems summarized their properties and established them as a field of study.
20世纪80年代后期，珀尔的《智能系统中的概率推理》和那不勒斯的《专家系统中的概率推理》总结了它们的性质，并将它们确立为一个研究领域。
== See also又及 ==
== See also又及 ==