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== In terms of PMFs for discrete distributions ==

== 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:<ref name=cover1991>{{cite book|last1=Cover|first1=T.M.|last2=Thomas|first2=J.A.|title=Elements of Information Theory|url=https://archive.org/details/elementsofinform0000cove|url-access=registration|date=1991|isbn=978-0-471-24195-9|edition=Wiley}}</ref>{{rp|20}}

The mutual information of two jointly discrete random variables <math>X</math> and <math>Y</math> is calculated as a double sum:<ref name=cover1991>{{cite book|last1=Cover|first1=T.M.|last2=Thomas|first2=J.A.|title=Elements of Information Theory|url=https://archive.org/details/elementsofinform0000cove|url-access=registration|date=1991|isbn=978-0-471-24195-9|edition=Wiley}}</ref>{{rp|20}}

数学 p {(x，y)} / math 是数学 x / math 和数学 y / math 的概率质量函数，而数学 p x / math 和数学 p y / math 分别是数学 x / math 和数学 y / math 的边际概率质量函数。

数学 p {(x，y)} / math 是数学 x / math 和数学 y / math 的概率质量函数，而数学 p x / math 和数学 p y / math 分别是数学 x / math 和数学 y / math 的边际概率质量函数。

== In terms of PDFs for continuous distributions ==

== In terms of PDFs for continuous distributions ==