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== Definition ==

== Definition 定义 ==

The conditional entropy of <math>Y</math> given <math>X</math> is defined as

The conditional entropy of <math>Y</math> given <math>X</math> is defined as

对该定义的直观解释是：根据定义<math>\displaystyle H( Y|X) =\mathbb{E}( \ f( X,Y) \ )</math>，其中<math>\displaystyle f:( x,y) \ \rightarrow -\log( \ p( y|x) \ ) </math>. <math>\displaystyle f</math>将给定<math>\displaystyle (X=x)</math>的<math>\displaystyle ( Y=y)</math>的信息内容与<math>\displaystyle ( x,y)</math>相关联，这是描述在给定<math>(X=x)</math>条件下的事件<math>\displaystyle (Y=y)</math>所需的信息量。根据大数定律，<math>H（Y ǀ X）</math>是<math>\displaystyle f(X,Y)</math>的大量独立实现的算术平均值。

对该定义的直观解释是：根据定义<math>\displaystyle H( Y|X) =\mathbb{E}( \ f( X,Y) \ )</math>，其中<math>\displaystyle f:( x,y) \ \rightarrow -\log( \ p( y|x) \ ) </math>. <math>\displaystyle f</math>将给定<math>\displaystyle (X=x)</math>的<math>\displaystyle ( Y=y)</math>的信息内容与<math>\displaystyle ( x,y)</math>相关联，这是描述在给定<math>(X=x)</math>条件下的事件<math>\displaystyle (Y=y)</math>所需的信息量。根据大数定律，<math>H（Y ǀ X）</math>是<math>\displaystyle f(X,Y)</math>的大量独立实现的算术平均值。

== Motivation ==

== Motivation 动机 ==

Let <math>H（Y ǀ X = x）</math> be the [[Shannon Entropy|entropy]] of the discrete random variable <math>Y</math> conditioned on the discrete random variable <math>X</math> taking a certain value <math>x</math>. Denote the support sets of <math>X</math> and <math>Y</math> by <math>\mathcal X</math> and <math>\mathcal Y</math>. Let <math>Y</math> have [[probability mass function]] <math>p_Y{(y)}</math>. The unconditional entropy of <math>Y</math> is calculated as <math>H（Y）:=E[I（Y）</math>, i.e.

Let <math>H（Y ǀ X = x）</math> be the [[Shannon Entropy|entropy]] of the discrete random variable <math>Y</math> conditioned on the discrete random variable <math>X</math> taking a certain value <math>x</math>. Denote the support sets of <math>X</math> and <math>Y</math> by <math>\mathcal X</math> and <math>\mathcal Y</math>. Let <math>Y</math> have [[probability mass function]] <math>p_Y{(y)}</math>. The unconditional entropy of <math>Y</math> is calculated as <math>H（Y）:=E[I（Y）</math>, i.e.