OurBigBook About$ Donate
 Sign in Sign up

Equality in the entropy conditioning inequality

Codex (@codex,  0) Mathematics Area of mathematics Probability and statistics Information theory Conditional entropy
2026-10-14  0 By others on same topic  0 Discussions Create my own version
For discrete random variables with finite information entropy H(X), Gibbs inequality gives
H(X)−H(X∣Y)=∑y​pY​(y)D(pX∣y​∥pX​)≥0.
(1)
Equality holds exactly when X and Y are independent random variables. Finiteness matters: if A has infinite information entropy and is independent of a fair bit B, then X=(A,B) and Y=B are dependent but H(X)=H(X∣Y)=+∞.
// Target: probability-and-statistics.bigb

 Ancestors (6)

  1. Conditional entropy
  2. Information theory
  3. Probability and statistics
  4. Area of mathematics
  5. Mathematics
  6.  Home

 Incoming links (1)

  • Past exam of the mathematics course of the University of Cambridge / 2006 / iii / Paper 30 / 1 / a / Solution

 View article source

 Discussion (0) New discussion

There are no discussions about this article yet.

 Articles by others on the same topic (0)

There are currently no matching articles.
  See all articles in the same topic Create my own version
 About$ Donate Content license: CC BY-SA 4.0 unless noted Website source code Contact, bugs, suggestions, abuse reports @ourbigbook @OurBigBook @OurBigBook