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In [[information theory]], '''joint [[entropy (information theory)|entropy]]''' is a measure of the uncertainty associated with a set of [[random variables|variables]].<ref name=korn>{{cite book |author1=Theresa M. Korn |author2=Korn, Granino Arthur |title=Mathematical Handbook for Scientists and Engineers: Definitions, Theorems, and Formulas for Reference and Review |publisher=Dover Publications |location=New York |year= |isbn=0-486-41147-8 |oclc= |doi=}}</ref>
In [[information theory]], '''joint [[entropy (information theory)|entropy]]''' is a measure of the uncertainty associated with a set of [[random variables|variables]].<ref name=korn>{{cite book |author1=Theresa M. Korn |author2=Korn, Granino Arthur |title=Mathematical Handbook for Scientists and Engineers: Definitions, Theorems, and Formulas for Reference and Review |publisher=Dover Publications |location=New York |year= |isbn=0-486-41147-8 |oclc= |doi=}}</ref>
==Definition==
==Definition 定义 ==
The joint [[Shannon entropy]] (in [[bit]]s) of two discrete [[random variable|random variables]] <math>X</math> and <math>Y</math> with images <math>\mathcal X</math> and <math>\mathcal Y</math> is defined as<ref name=cover1991>{{cite book |author1=Thomas M. Cover |author2=Joy A. Thomas |title=Elements of Information Theory |publisher=Wiley |location=Hoboken, New Jersey |year= |isbn=0-471-24195-4}}</ref>{{rp|16}}
The joint [[Shannon entropy]] (in [[bit]]s) of two discrete [[random variable|random variables]] <math>X</math> and <math>Y</math> with images <math>\mathcal X</math> and <math>\mathcal Y</math> is defined as<ref name=cover1991>{{cite book |author1=Thomas M. Cover |author2=Joy A. Thomas |title=Elements of Information Theory |publisher=Wiley |location=Hoboken, New Jersey |year= |isbn=0-471-24195-4}}</ref>{{rp|16}}
具有像<math>\mathcal X</math>和<math>\mathcal Y</math>的两个离散随机变量<math>X</math>和<math>Y</math>的'''<font color="#ff8000"> 联合香农熵Shannon entropy </font>'''（以比特为单位）定义为：
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where <math>x</math> and <math>y</math> are particular values of <math>X</math> and <math>Y</math>, respectively, <math>P(x,y)</math> is the [[joint probability]] of these values occurring together, and <math>P(x,y) \log_2[P(x,y)]</math> is defined to be 0 if <math>P(x,y)=0</math>.
where <math>x</math> and <math>y</math> are particular values of <math>X</math> and <math>Y</math>, respectively, <math>P(x,y)</math> is the [[joint probability]] of these values occurring together, and <math>P(x,y) \log_2[P(x,y)]</math> is defined to be 0 if <math>P(x,y)=0</math>.