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.
Conditional mutual information by a same-marginal product law
Definition
Assume AC. Let X,Y,Z be random elements with standard-Borel value spaces and write for the joint law of in that coordinate order. Let and be regular conditional laws of X and Y given Z=z. Let R be the probability law on the same product space whose rectangle values are Its existence, uniqueness, and independence of the versions K,L are established in The same-marginal conditional-product law is well-defined. Define the conditional mutual information by The reference R has the same Z marginal as the joint law and makes X,Y independent conditionally on Z. The logarithm is natural, so finite values are measured in nats under Information units and logarithm-base convention. The definition is a divergence between full probability laws; it does not subtract two possibly infinite entropies.
Depends on
- Kullback–Leibler divergence
- Information units and logarithm-base convention
- The Axiom of Choice
- The same-marginal conditional-product law is well-defined
- Conditional law given a random element
- Existence of regular conditional distributions for standard borel targets
- Regular conditional kernels factor through a standard borel conditioning variable
- Disintegration of a joint law on standard borel spaces
- Standard Borel spaces
- Law or distribution of a random element
Used by
- Changing a conditional kernel on a null fiber leaves information unchanged Example
- Coarsening the conditioning variable can increase conditional mutual information Example
- Conditional mutual-information chain rule and data processing for standard-Borel variables 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 is recovered by finite coordinate quantizations Theorem
- Mutual-information chain rule without subtracting infinities Theorem
- Zero information is exactly independence Theorem
Dependency tree · two levels
60 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
- Yury Polyanskiy and Yihong Wu, MIT 6.441 Information Theory, Chapter 2 §2.4 (standard reference, not scraped)
- Robert M. Gray, Entropy and Information Theory, corrected author-hosted first edition, Chapter 5 §5.5 (standard reference, not scraped)