What is Joint Entropy in Classical Information Theory?
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Definition1
Let’s consider two discrete random variables $X, Y$ with their joint probability mass function $p$. The joint entropy of $X$ and $Y$ is defined as follows.
$$ H(X, Y) := - \sum_{i,j} p(x_{i}, y_{j}) \log_{2}p(x_{i}, y_{j}) $$
Explanation
The definition of entropy simply changes to involve the joint probability mass function.
Stephen M. Barnett, Quantum Information (2009), p10-11 ↩︎