Conditional entropy

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Venn diagram for various information measures associated with correlated variables X and Y. The area contained by both circles is the joint entropy H(X,Y). The circle on the left (red and violet) is the individual entropy H(X), with the red being the conditional entropy H(X|Y). The circle on the right (blue and violet) is H(Y), with the blue being H(Y|X). The violet is the mutual information I(X;Y).

In information theory, the conditional entropy (or equivocation) quantifies the amount of information needed to describe the outcome of a random variable given that the value of another random variable is known. Here, information is measured in shannons, nats, or hartleys. The entropy of conditioned on is written as .


If is the entropy of the discrete random variable conditioned on the discrete random variable taking a certain value , then is the result of averaging over all possible values that may take.

Given discrete random variables with image and with image , the conditional entropy of given is defined as the weighted sum of for each possible value of , using as the weights:[1]

Note: It is understood that the expressions 0 log 0 and 0 log (c/0) for fixed c>0 should be treated as being equal to zero.


Conditional entropy equals zero[edit]

if and only if the value of is completely determined by the value of .

Conditional entropy of independent random variables[edit]

Conversely, if and only if and are independent random variables.

Chain rule[edit]

Assume that the combined system determined by two random variables X and Y has joint entropy , that is, we need bits of information on average to describe its exact state. Now if we first learn the value of , we have gained bits of information. Once is known, we only need bits to describe the state of the whole system. This quantity is exactly , which gives the chain rule of conditional entropy:

The chain rule follows from the above definition of conditional entropy:

In general, a chain rule for multiple random variables holds:

It has a similar form to Chain rule (probability) in probability theory, except that addition instead of multiplication is used.

Bayes' rule[edit]

Bayes' rule for conditional entropy states

Proof. and . Symmetry entails . Subtracting the two equations implies Bayes' rule.

If Y is conditionally independent of Z given X we have:

Other properties[edit]

For any and :

where is the mutual information between and .

For independent and :

Although the specific-conditional entropy, , can be either less or greater than , can never exceed .

Conditional differential entropy[edit]


The above definition is for discrete random variables and no more valid in the case of continuous random variables. The continuous version of discrete conditional entropy is called conditional differential (or continuous) entropy. Let and be a continuous random variables with a joint probability density function . The differential conditional entropy is defined as



In contrast to the conditional entropy for discrete random variables, the conditional differential entropy may be negative.

As in the discrete case there is a chain rule for differential entropy:

Notice however that this rule may not be true if the involved differential entropies do not exist or are infinite.

Joint differential entropy is also used in the definition of the mutal information between continuous random variables:

Relation to estimator error[edit]

The conditional differential entropy yields a lower bound on the expected squared error of an estimator. For any random variable , observation and estimator the following holds:[1]

Generalization to quantum theory[edit]

In quantum information theory, the conditional entropy is generalized to the conditional quantum entropy. The latter can take negative values, unlike its classical counterpart.

See also[edit]


  1. ^ a b T. Cover; J. Thomas (1991). Elements of Information Theory (PDF). p. 15. ISBN 0-471-06259-6.