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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}}

联合香农熵Shannon entropy </font>'''的定义是：以比特为单位，具有<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>'''<font color="#ff8000"> 有

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{{Equation box 1