Data-processing inequality
If finite X→Y→Z form a Markov chain, meaning Z is conditionally independent of X given Y, then I(X;Z)≤I(X;Y).
Key knowledge
If finite X→Y→Z form a Markov chain, meaning Z is conditionally independent of X given Y, then I(X;Z)≤I(X;Y). Processing the observation cannot increase its mutual information about the original variable.
Conditions and scope
- The stipulated conditional independence is essential.
- This is not a claim that arbitrary randomized processing always decreases the output's entropy.
Sources · 1
References
Sources retain their recorded scope. A source link alone does not establish verification of every statement.
Evidence and review
Lecture 2 Markov-relation side-information inequality and mutual-information identity.
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Entry details
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