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In information theory, joint entropy is a measure of the uncertainty associated with a set of variables.
Contents
Definition
The joint Shannon entropy (in bits) of two variables
where
For more than two variables
where
Greater than individual entropies
The joint entropy of a set of variables is greater than or equal to all of the individual entropies of the variables in the set.
Less than or equal to the sum of individual entropies
The joint entropy of a set of variables is less than or equal to the sum of the individual entropies of the variables in the set. This is an example of subadditivity. This inequality is an equality if and only if
Relations to other entropy measures
Joint entropy is used in the definition of conditional entropy
and
In quantum information theory, the joint entropy is generalized into the joint quantum entropy.