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.
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These labels describe origin, not correctness: citations and verification chips remain separate evidence.
Finite Pinsker inequality in Euclidean norm
Statement
Let and be probability vectors on a finite nonempty set. With natural logarithms and when , one has
Facts & Assumptions
Given: Finite probability vectors and the finite KL convention in the Statement.
Finite log-sum inequality with support and equality cases: grouping coordinates into two classes cannot increase the finite KL divergence; the grouped divergence is at most the original divergence, including the stated support conventions.
Finite Kullback–Leibler divergence: is the sum of over positive , and equals if a positive lies over .
The natural logarithm has derivative 1/x and equals the integral from 1 to x of 1/t and Sums, scalar multiples, products and quotients: , , , and when : logarithmic differentiation and the product rule apply on positive coordinates.
Proof
If , the first inequality is immediate because its left side is . Otherwise let and . Since , the positive and negative coordinate differences have equal total magnitude, so . If is empty, then and the claim holds.
For and its complement, [F1] gives , where and , with endpoint terms interpreted by the same support convention.
If , finite divergence and force , so the binary divergence is zero and . For , define . On , [F3] and the product rule give and . Thus is convex and minimized at ; continuity of at zero from [F4] extends to .
Therefore . Since for every finite vector , the Euclidean bound follows.
Depends on
- Finite log-sum inequality with support and equality cases
- Finite Kullback–Leibler divergence
- Finite probability vectors
- The natural logarithm has derivative 1/x and equals the integral from 1 to x of 1/t
- Sums, scalar multiples, products and quotients: $(f+g)'(c) = f'(c) + g'(c)$, $(\alpha f)'(c) = \alpha f'(c)$, $(fg)'(c) = f'(c)g(c) + f(c)g'(c)$, and $(f/g)'(c) = \bigl(f'(c)g(c) - f(c)g'(c)\bigr)/g(c)^{2}$ when $g(c) \ne 0$
- The logarithm grows more slowly than every positive real power
Used by
Dependency tree · two levels
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Sources
- Yury Polyanskiy and Yihong Wu, Information Theory: From Coding to Learning, author prepublication (standard reference, not scraped)