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Mutual-information chain rule without subtracting infinities
Statement
Assume AC. For standard-Borel random elements X,Y,Z, The right side is a sum in . This chain rule is asserted as an equality of extended nonnegative quantities; no difference of possibly infinite entropies or divergences occurs.
Facts & Assumptions
Given: AC and standard-Borel random elements X,Y,Z on one probability space.
AC is required for the Radon–Nikodym divergences and standard-Borel conditional laws. The Axiom of Choice
Mutual information is the KL divergence of a joint law from the product of its marginals. Measure-theoretic mutual information
Conditional mutual information is the KL divergence from the same-Z-marginal conditionally independent product law. Conditional mutual information by a same-marginal product law
For disintegrated joint laws, KL equals the marginal KL plus the average conditional KL, as an extended sum. KL chain rule for standard-Borel disintegrations
The same-marginal conditional-product law exists, has the stated rectangle formula, and is independent of kernel versions. The same-marginal conditional-product law is well-defined
Standard-Borel joint laws admit the regular conditional distributions and disintegration identities used below. Disintegration of a joint law on standard borel spaces
The product of supplied probability kernels is a probability kernel, and KL is invariant under a measurable coordinate bijection. The product of two probability kernels is a probability kernel KL is invariant under a measurable bijection
Proof
Given: The hypotheses in the Statement.
Let be a regular conditional law of X given Z and let on . In the coordinate order , Q has Z marginal and conditional law for . On every rectangle , the law formed from this base and kernel has mass so it equals Q by rectangle uniqueness.
Apply [F3] to the joint law of and Q using their common Z marginal. With denoting the conditional law of given Z=z, this gives The formula is an extended sum of nonnegative KL values.
For -almost every z, disintegrate the two measures in step 2.1 over Y. Their Y marginals are and , while their conditional X laws are and , respectively. [F3] gives Applying [F3] with the common Y marginal identifies the last integral as .
Integrate step 3.1 over . The first term becomes , which equals by the measurable coordinate swap [F6]. Applying [F3] with the common Z marginal of the joint law and its conditional-product reference shows that the second term integrates to as defined in [F2]. All terms are nonnegative KL divergences, so their extended sum is well-defined even when one or more are infinite.
Depends on
- Measure-theoretic mutual information
- Conditional mutual information by a same-marginal product law
- KL chain rule for standard-Borel disintegrations
- The same-marginal conditional-product law is well-defined
- The product of two probability kernels is a probability kernel
- Disintegration of a joint law on standard borel spaces
- Finite products of standard Borel spaces are standard Borel
- KL is invariant under a measurable bijection
- The Axiom of Choice
Used by
- 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
Dependency tree · two levels
58 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 §§5.3 and 5.5 (standard reference, not scraped)
- John Duchi, Lecture Notes on Statistics and Information Theory, Chapter 2 §2.1 (standard reference, not scraped)