Data processing inequality

The data processing inequality is an information theoretic concept which states that the information content of a signal cannot be increased via a local physical operation. This can be expressed concisely as 'post-processing cannot increase information'.

Definition

Let three random variables form the Markov chain $X\rightarrow Y\rightarrow Z$, implying that the conditional distribution of $Z$ depends only on $Y$ and is conditionally independent of $X$. Specifically, we have such a Markov chain if the joint probability mass function can be written as

$p(x,y,z)=p(x)p(y|x)p(z|y)$

In this setting, no processing of Y , deterministic or random, can increase the information that Y contains about X. Using the mutual information, this can be written as :

$I(X;Y)\geqslant I(X;Z)$

With the equality $I(X;Y)=I(X;Z)$ if and only if $I(X;Y\mid Z)=0$, i.e. $Z$ and $Y$ contain the same information about $X$, and $X\rightarrow Z\rightarrow Y$ also forms a Markov chain.

Proof

One can apply the chain rule for mutual information to obtain two different decompositions of $I(X;Y,Z)$:

$I(X;Z)+I(X;Y\mid Z)=I(X;Y,Z)=I(X;Y)+I(X;Z\mid Y)$

By the relationship $X\rightarrow Y\rightarrow Z$, we know that $X$ and $Z$ are conditionally independent, given $Y$, which means the conditional mutual information, $I(X;Z\mid Y)=0$. The data processing inequality then follows from the non-negativity of $I(X;Y\mid Z)\geq 0$.

See also

• Garbage in, garbage out

External links

• http://www.scholarpedia.org/article/Mutual_information