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Additivity of KL divergence for finite product laws
Statement
Assume AC. Let with . For each , let be a measurable space and let be probability measures on it. On the finite product measurable space set With the KL convention of Kullback–Leibler divergence, where a sum with an infinite term is .
Facts & Assumptions
Given: AC, a natural number , measurable spaces for , probability laws , and their finite product measures .
The Axiom of Choice: AC is assumed by the Radon–Nikodym suppliers in the KL definition.
Kullback–Leibler divergence and The Radon-Nikodym derivative as an almost-everywhere equivalence class: if , its density is an almost-everywhere class satisfying , and ; the negative part is finite.
The product sigma-algebra and its finite iterates and The product measure of two sigma-finite measure spaces: finite products use the iterated product sigma-algebra and product measures whose rectangle values are the products of factor measures.
For sigma-finite factors, the product measure exists, has the rectangle formula, is sigma-finite, and is unique and Measures agreeing on a generating pi-system are equal under an increasing finite-measure exhaustion from that pi-system: product measures have the rectangle formula and are determined by it on the generating rectangles.
The measure with density relative to and The indefinite integral of a nonnegative measurable function is a measure: a nonnegative measurable density defines the measure by .
Tonelli's theorem for nonnegative measurable functions on a sigma-finite product and Fubini's theorem for L^1 functions on a sigma-finite product: nonnegative product integrals factor by Tonelli, and integrable product functions may be integrated sectionwise by Fubini.
Proof
If for some , choose with . Let , where and for . The product rectangle has and by [F2, F3]. Hence , so the left side is ; the corresponding term on the right is also and the equality holds.
Now suppose for every , and choose nonnegative representatives . Define . Each coordinate function is measurable by the finite product sigma-algebra definition: its level-set preimages are measurable rectangles. Hence the vector of coordinate values is measurable, since rectangles generate the Borel sigma-algebra of ; composing with the continuous multiplication map gives measurable . Repeated Tonelli and the rectangle formulas give for every measurable rectangle ; applying the same calculation to the full space gives .
The measure is a probability measure by [F4] and step 1.2. The product rectangles form a generating pi-system and the finite product spaces are sigma-finite, so [F3] and the rectangle identity in step 1.2 imply on the entire product sigma-algebra. Therefore as a Radon–Nikodym class.
Each is finite -almost everywhere because , and ; thus -almost everywhere. Therefore [F6] gives -almost everywhere. Each has integrable negative part by [F1], and the negative part of their finite sum is bounded by the sum of those negative parts. Split each summand into positive and negative parts: Tonelli factors each nonnegative part through the product law, and Fubini applies to the negative parts, which are integrable. Hence, allowing for positive parts, By [F1] and step 2.1 the left side is , proving the claim.
Depends on
- The Axiom of Choice
- Finite, sigma-finite, and semifinite measures
- Kullback–Leibler divergence
- The measure with density $f$ relative to $\mu$
- Probability measures and probability spaces
- The product measure of two sigma-finite measure spaces
- The product sigma-algebra and its finite iterates
- The Radon-Nikodym derivative as an almost-everywhere equivalence class
- Order, continuity, range, and the product, quotient, and reciprocal laws for the natural logarithm
- Fubini's theorem for L^1 functions on a sigma-finite product
- The indefinite integral of a nonnegative measurable function is a measure
- Measures agreeing on a generating pi-system are equal under an increasing finite-measure exhaustion from that pi-system
- For sigma-finite factors, the product measure exists, has the rectangle formula, is sigma-finite, and is unique
- Tonelli's theorem for nonnegative measurable functions on a sigma-finite product
Used by
- A dense Gaussian mean lower bound by Assouad’s method Example
- A Gaussian two-point lower bound Example
- A sparse Gaussian sequence lower bound by Fano’s method Example
- Clean block-sparse Gaussian covariance has squared operator lower rate s log(ep/s) over n Lemma
- Clean Gaussian covariance has a capped squared operator lower rate Lemma
- Diagonal covariance lower bound with bounded-risk saturation Lemma
- Gaussian sparse-sequence minimax lower bound Theorem
Dependency tree · two levels
61 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
- Philippe Rigollet and Jan-Christian Hütter, High-Dimensional Statistics: Lecture Notes, Chapter 4, Proposition 4.6 (standard reference, not scraped)
- John Duchi, Lecture Notes on Statistics and Information Theory, Chapter 8, §8.2.2, equation (8.2.3) (standard reference, not scraped)