# Mutual Information

## Alexei Gilchrist

Mutual information.

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### 1 Definition

The mutual information \(I(X;Y)\) for \(x\in X\) and \(y\in Y\), measures the `distance’ between the full joint probability distribution \(p(xy)\), and the probability distribution that assumes \(x\) and \(y\) are independent \(p(x)p(y)\). That is, in terms of the mutual entropy,

\[\begin{equation*}D(p(xy)||p(x)p(y)) = \sum_{x,y} p(x,y)\log\frac{p(x,y)}{p(x)p(y)} \equiv I(X;Y)\end{equation*}\]

\(I(X;Y)\) is a measure of how much information \(X\) contains about \(Y\) (and vice versa).

### 2 Properties

- Symmetric: \(I(X;Y)=I(Y;X)\)
- \(I(X;Y)\ge 0\). This follows immediately from the relative entropy since that is non-negative.
- If \(I(X;Y)=0\) then \(x\) and \(y\) are independent

### 3 Relationship to entropies

The chain rule for entropies is

\[\begin{equation*}H(X,Y) = H(X|Y)+ H(Y) = H(Y|X) + H(X)\end{equation*}\]

but we can write the mutual information also in terms of these entropies \[\begin{align*}I(X;Y) &= \sum_{x,y} p(x,y)\log\frac{p(x,y)}{p(x)p(y)} \\
&= \sum_{x,y} p(x,y)\log\frac{p(x|y)p(y)}{p(x)p(y)} \\
&= \sum_{x,y} p(x,y)\log p(x|y)-\sum_{x,y} p(x,y)\log p(x)\\
&= -H(X|Y) + H(X)\end{align*}\]

and because the mutual information is symmetric we have \[\begin{equation*}I(X;Y) = H(X)-H(X|Y) = H(Y)-H(Y|X),\end{equation*}\]

and using the joint entropy, \[\begin{equation*}I(X;Y) = H(X)+H(Y)-H(X,Y).\end{equation*}\]

The relationship between the mutual information and the various entropies is summarised in the diagram below.

Note that

\[\begin{equation*}I(X;X) = H(X)-H(X|X) = H(X)\end{equation*}\]

Since \(p(x|x)=1\) and so \(H(X|X)=0\).