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Lucas Fidon edited subsubsection_Mutual_Information_There_are__.tex
almost 8 years ago
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The most intuitive definition is the following:
Let $X : P_{1} \rightarrow E_{1}$ and $Y: P_{2} \rightarrow E_{2}$ be two random variables, where $E_{1}$ and $E_{2}$ are two discrete probability spaces.
We define the Mutual information of $X$ and $Y$, noted $I(X,Y)$, as:
\[ I(X,Y) = \sum_{(x,y) \in
E_1xE_2}P_{(X,Y)}(x,y)*log(\frac{log(P_{(X,Y)}(x,y))}{P_{X}(x)*P_{Y}(y)}) E_1*E_2}P_{(X,Y)}(x,y)*log(\frac{log(P_{(X,Y)}(x,y))}{P_{X}(x)*P_{Y}(y)}) \]