The data processing inequality is an information theoretic concept that 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'.^[1]

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

${\displaystyle p(x,y,z)=p(x)p(y|x)p(z|y)=p(y)p(x|y)p(z|y)}$

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

${\displaystyle I(X;Y)\geqslant I(X;Z),}$

with the equality ${\displaystyle I(X;Y)=I(X;Z)}$ if and only if ${\displaystyle I(X;Y\mid Z)=0}$. That is, ${\displaystyle Z}$ and ${\displaystyle Y}$ contain the same information about ${\displaystyle X}$, and ${\displaystyle X\rightarrow Z\rightarrow Y}$ also forms a Markov chain.^[2]

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

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

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

• Garbage in, garbage out

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

This computer science article is a stub. You can help Wikipedia by adding missing information.

• v
• t
• e