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In [[information theory]], the '''conditional entropy''' quantifies the amount of information needed to describe the outcome of a [[random variable]] <math>Y</math> given that the value of another random variable <math>X</math> is known. Here, information is measured in [[Shannon (unit)|shannon]]s, [[Nat (unit)|nat]]s, or [[Hartley (unit)|hartley]]s. The ''entropy of <math>Y</math> conditioned on <math>X</math>'' is written as H（X ǀ Y）.

In [[information theory]], the '''conditional entropy''' quantifies the amount of information needed to describe the outcome of a [[random variable]] <math>Y</math> given that the value of another random variable <math>X</math> is known. Here, information is measured in [[Shannon (unit)|shannon]]s, [[Nat (unit)|nat]]s, or [[Hartley (unit)|hartley]]s. The ''entropy of <math>Y</math> conditioned on <math>X</math>'' is written as H（X ǀ Y）.

在'''<font color="#ff8000"> 信息论Information theory</font>'''中，假设随机变量<math>X</math>的值已知，那么'''<font color="#ff8000"> 条件熵Conditional entropy</font>'''则用于量化描述随机变量<math>Y</math>的结果所需的信息量。此时，信息以'''<font color="#ff8000"> 香农Shannon </font>'''，'''<font color="#ff8000"> 奈特nat</font>'''或'''<font color="#ff8000"> 哈特莱hartley</font>'''衡量。以<math>X</math>为条件的<math>Y</math>熵写为H（X ǀ Y）。

在'''<font color="#ff8000"> 信息论Information theory</font>'''中，假设随机变量<math>X</math>的值已知，那么'''<font color="#ff8000"> 条件熵Conditional entropy</font>'''则用于量化描述随机变量<math>Y</math>的结果所需的信息量。此时，信息以'''<font color="#ff8000"> 香农Shannon </font>'''，'''<font color="#ff8000"> 奈特nat</font>'''或'''<font color="#ff8000"> 哈特莱hartley</font>'''衡量。以<math>X</math>为条件的<math>Y</math>熵写为<math>H（X ǀ Y）</math>。