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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}}

联合熵Shannon entropy </font>'''的定义是：以比特为单位，对于具有<math>\mathcal X</math>和<math>\mathcal Y</math>的两个离散随机变量函数<math>X</math>和<math>Y</math>'''有<font color="#ff8000">

联合熵Shannon entropy </font>'''的定义是：以比特为单位，对于具有<math>\mathcal X</math>和<math>\mathcal Y</math>的两个离散随机变量函数<math>X</math>和<math>Y</math>'''有

{{Equation box 1

{{Equation box 1

|equation = {{NumBlk||<math>\Eta(X,Y) = -\sum_{x\in\mathcal X} \sum_{y\in\mathcal Y} P(x,y) \log_2[P(x,y)]</math>|{{EquationRef|Eq.1}}}}

|equation = {{NumBlk||<math>\Eta(X,Y) = -\sum_{x\in\mathcal X} \sum_{y\in\mathcal Y} P(x,y) \log_2[P(x,y)]</math>|{{EquationRef|Eq.1}}}}

|cellpadding= 6

|cellpadding= 6