feat(InformationTheory/KullbackLeibler): finite Kullback-Leibler divergence - #43104
feat(InformationTheory/KullbackLeibler): finite Kullback-Leibler divergence#43104elazarg wants to merge 3 commits into
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PR summary 2a5c657e96Import changes for modified filesNo significant changes to the import graph Import changes for all files
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| Current number | Change | Type (weak) |
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| exposed public sections | 4969 | 1 |
Current commit 2a5c657e96
Reference commit d8ffbd6ba0
This script lives in the mathlib-ci repository. To run it locally, from your mathlib4 directory:
git clone https://github.com/leanprover-community/mathlib-ci.git ../mathlib-ci
../mathlib-ci/scripts/reporting/technical-debt-metrics.py pr_summary
- The
relativevalue is the weighted sum of the differences with weight given by the inverse of the current value of the statistic. - The
absolutevalue is therelativevalue divided by the total sum of the inverses of the current values (i.e. the weighted average of the differences).
Define
klDivFin p q = ∑ i, p i * log (p i / q i)forp q : ι → ℝon aFintype, and prove Gibbs' inequality for it. The weights are not assumed to be normalized:(∑ i, p i) - (∑ i, q i) ≤ klDivFin p qneeds only nonnegativity and absolute continuity, and0 ≤ klDivFin p qfollows whenever∑ i, q i ≤ ∑ i, p i.Two supporting lemmas added to
Analysis/SpecialFunctions/Log/NegMulLog:Real.mul_log_divandReal.sub_le_mul_log_div, a quotient form ofReal.self_sub_one_le_mul_log.Continuing #42584, another part of https://github.com/elazarg/kraft
klDivFinis aFinset.sumdefinition, not a specialization ofklDiv.klDivisℝ≥0∞-valued and defined throughllrandMeasure.rnDeriv, and Mathlib currently has no formula forMeasure.rnDerivon a discrete space.Even with that lemma in place,
klDivFinisℝ-valued, so it composes directly withFinsetarithmetic in downstream code; and it requires no normalization - Gibbs' inequality here holds under∑ i, q i ≤ ∑ i, p i, so no probability measures are needed.Happy to split the two
NegMulLoglemmas into a separate PR if needed.