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→关于离散分布的PMF In terms of PMFs for discrete distributions
The mutual information of two jointly discrete random variables <math>X</math> and <math>Y</math> is calculated as a double sum:
The mutual information of two jointly discrete random variables <math>X</math> and <math>Y</math> is calculated as a double sum:
两个联合分布的离散型随机变量X和Y的互信息计算表现为双和的形式：
[[文件:MI pic2.png|居中|缩略图]]
[[文件:MI pic2.png|居中|缩略图]]
where <math>p_{(X,Y)}</math> is the joint probability mass function of <math>X</math> and <math>Y</math>, and <math>p_X</math> and <math>p_Y</math> are the marginal probability mass functions of <math>X</math> and <math>Y</math> respectively.
where <math>p_{(X,Y)}</math> is the joint probability mass function of <math>X</math> and <math>Y</math>, and <math>p_X</math> and <math>p_Y</math> are the marginal probability mass functions of <math>X</math> and <math>Y</math> respectively.
<math>p_{(X,Y)}</math>是<math>X</math>和<math>Y</math>的'''概率质量函数 Probability mass functions'''，而<math>p_X</math>和<math>p_Y</math>分别是数学<math>X</math>和<math>Y</math>的'''边缘概率质量函数 Marginal probability mass functions'''。
<math>p_{(X,Y)}</math>是<math>X</math>和<math>Y</math>的'''<font color="#ff8000">概率质量函数 Probability mass functions</font>'''，而<math>p_X</math>和<math>p_Y</math>分别是数学<math>X</math>和<math>Y</math>的'''<font color="#ff8000">边缘概率质量函数 Marginal probability mass functions</font>'''。
== 连续分布的PDF In terms of PDFs for continuous distributions ==
== 连续分布的PDF In terms of PDFs for continuous distributions ==