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Finite-alphabet conditional entropy is version-invariant and obeys its chain rule
Statement
Assume AC. Let take values in finite nonempty sets and in a standard-Borel space. Conditional entropy as defined in Finite-alphabet conditional entropy given an observation is unchanged when any supplied regular conditional law is replaced by another version. Moreover, All three quantities are finite; the identity is an additive finite-sum identity.
Facts & Assumptions
Given: The random elements and hypotheses in the Statement.
AC is required for the stated regular conditional law existence and simultaneous uniqueness interfaces. The Axiom of Choice
is the integral of the finite Shannon entropy of a conditional probability vector; the value of at zero is zero. Finite-alphabet conditional entropy given an observation Finite distributions, entropy, divergence, and stochastic matrices
Two regular conditional distributions of a standard-Borel-valued variable agree as probability measures outside one measurable null set. Simultaneous ae uniqueness of regular conditional distributions
A joint law on standard-Borel factors disintegrates through a supplied regular conditional law, and the integral identity extends from rectangles to nonnegative product-measurable functions. Disintegration of a joint law on standard borel spaces
A finite product of standard-Borel spaces is standard Borel. Finite products of standard Borel spaces are standard Borel
Proof
Given: AC and the stated finite-valued random elements and standard-Borel conditioning variable.
If two versions of the conditional law of given are supplied, [F2] gives one -null set outside which their probability measures agree. Every atom probability, and hence the finite entropy sum in [F1], then agrees pointwise outside that set. Integrating gives the same . The same argument applies to and to .
Choose regular conditional laws of given and of given ; they exist by [A1, F3], since and are standard Borel by [F4]. Write , , and for the chosen versions. The function is itself a conditional law of given , so [F2] makes it agree almost everywhere with the version used in . For each and measurable in the value space of , the two disintegrations [F3] give Both integrands are bounded and measurable. Testing the sets where one exceeds the other by at least , for each , shows almost everywhere; because the alphabets are finite, one null set works for every .
Outside that null set, if then every , so the row contributes zero to the conditional entropy. If , then for every . On positive entries, ; multiplying by and summing over the finite alphabets gives
Integrate the finite conditional identity from step 2.1 with respect to . The disintegration identity [F3] identifies the last integral with and the other terms with their definitions. By the bound in [F1], each conditional entropy is bounded by its finite alphabet size divided by , so all integrals are finite and no subtraction of infinite quantities occurs. Step 1.1 proves the accompanying version-independence assertion.
Depends on
- Finite-alphabet conditional entropy given an observation
- Finite distributions, entropy, divergence, and stochastic matrices
- Finite conditional entropy
- Conditional law given a random element
- Disintegration of a joint law on standard borel spaces
- Simultaneous ae uniqueness of regular conditional distributions
- Finite products of standard Borel spaces are standard Borel
- The Axiom of Choice
Used by
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Sources
- Robert M. Gray, Entropy and Information Theory, corrected author-hosted first edition, Chapter 2 §2.5 and Chapter 5 §5.5 (standard reference, not scraped)
- John Duchi, Lecture Notes on Statistics and Information Theory, Chapter 2 §2.1 (standard reference, not scraped)