Conditional entropy - Wikipedia
In information theory, the conditional entropy quantifies the amount of information needed to describe the outcome of a random variable Y given that the value of another random variable X is known. Here, information is measured in shannons, nats, or hartleys. The entropy of Y conditioned on X is written as H(Y|X) .
Conditional entropy - Wikipedia Jump to content From Wikipedia, the free encyclopedia Measure of relative information in probability theory Venn diagram showing additive and subtractive relationships various information measures associated with correlated variables X {\displaystyle X} and Y {\displaystyle Y} . The area contained by both circles is the joint entropy H ( X , Y ) {\displaystyle \mathrm {H} (X,Y)} . The circle on the left (red and violet) is the individual entropy H ( X ) {\displaystyle \mathrm {H} (X)} , with the red being the conditional entropy H ( X | Y ) {\displaystyle \mathr
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