Joint entropy

In information theory, joint entropy is a measure of the uncertainty associated with a set of variables.

The joint Shannon entropy (in bits) of two discrete random variables ${\displaystyle X}$ and ${\displaystyle Y}$ is defined as

where ${\displaystyle x}$ and ${\displaystyle y}$ are particular values of ${\displaystyle X}$ and ${\displaystyle Y}$, respectively, ${\displaystyle P(x,y)}$ is the joint probability of these values occurring together, and ${\displaystyle P(x,y)\log _{2}[P(x,y)]}$ is defined to be 0 if ${\displaystyle P(x,y)=0}$.

...
Wikipedia