How statement and proof provenance work
The first chip identifies the source of the statement or construction; the second identifies the source of its local proof or verification.
- Literature-sourced: the exact statement appears in a cited source; only wording and notation differ.
- AI-adapted: a semantically identical restatement of literature-sourced material, modulo indexing, notation, and boundary cases adopted by the library.
- AI-generated: a genuinely novel statement formulated by AI, with no source for the claim itself.
These labels describe origin, not correctness: citations and verification chips remain separate evidence.
KL is invariant under a measurable bijection
Statement
Let be a measurable bijection with measurable inverse. For probability measures P,Q on E, let be their push-forwards. Then
Facts & Assumptions
Given: AC, the measurable isomorphism , and probability measures P,Q on E.
AC supplies the Radon–Nikodym density in the absolutely continuous branch of KL. The Axiom of Choice
KL is outside absolute continuity; under absolute continuity it is the integral of the log Radon–Nikodym density. Kullback–Leibler divergence
Push-forward integration satisfies for every nonnegative measurable g, and for integrable signed g. Change of variables for expectation
Proof
Given: The hypotheses in the Statement.
If , choose E_0 with . Since is measurable, is measurable; then . Both divergences are by [F1].
If , let . [given] The function is measurable. For every B in F, so s is a Radon–Nikodym density of with respect to . By [F2], the push-forward integral identity applies separately to the nonnegative functions and ; the negative-part integrals are finite by [F1]. Therefore including the case where the positive-part integral is .
Depends on
Used by
- Conditional mutual-information chain rule and data processing for standard-Borel variables Lemma
- Gaussian reference-kernel mutual-information lower bound Lemma
- Mutual information tensorizes over a memoryless vector channel Lemma
- Random-cut stationary ergodic block-channel approximation Lemma
- Finite-dimensional Gaussian vector squared-error rate-distortion function Theorem
- Mutual-information chain rule without subtracting infinities Theorem
Dependency tree · two levels
36 results within two dependency steps of this one, each drawn at its shortest distance from it. An arrow runs from a result to what uses it, so the chart reads left to right and ends at this result, which carries a heavier outline. Every node is a link to that result. Click elsewhere on the chart to enlarge it.
Sources
- Robert M. Gray, Entropy and Information Theory, corrected author-hosted first edition, Chapter 5 §5.2 (standard reference, not scraped)
- Yury Polyanskiy and Yihong Wu, MIT 6.441 Information Theory, Chapter 3 §3.1 (standard reference, not scraped)