InformationTheory

InformationTheory.Shannon.Pinsker.Sharp

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Sharp Pinsker inequality (constant 1/√2) #

For probability measures P, Q on a finite alphabet α with P ≪ Q, the sharp Cover–Thomas form of Pinsker's inequality: tvNorm P Q ≤ √((klDiv P Q).toReal / 2). The total-variation norm tvNorm is shared with the weak form in Pinsker/Basic.lean.

Main statements #

Implementation notes #

The pointwise bound is obtained from the auxiliary H t := 2 (t + 2) · klFun t - 3 (t - 1)^2, whose second derivative 4 (log t + 1/t - 1) is nonnegative on (0, ∞) and which has a minimum of 0 at t = 1. The global bound follows by a per-element application together with the Cauchy–Schwarz step and ∑ (p + 2q) = 3.

References #

  • T. M. Cover and J. A. Thomas, Elements of Information Theory (2nd ed.), Wiley, 2006.

Pointwise sharp Pinsker inequality #

theorem

InformationTheory.Shannon.PinskerSharp.klFun_sharp_lower

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(t : ) (ht : 0 t) :
3 * (t - 1) ^ 2 2 * (t + 2) * klFun t

The pointwise sharp Pinsker inequality 3 · (t - 1)^2 ≤ 2 · (t + 2) · klFun t for t ≥ 0.

Used by

    Sharp Pinsker inequality #

    theorem

    InformationTheory.Shannon.PinskerSharp.tvNorm_le_sqrt_klDiv_div_two

    source

    Pinsker's inequality (sharp form, constant 1/√2): for probability measures P ≪ Q on a finite alphabet, tvNorm P Q ≤ √((klDiv P Q).toReal / 2).

    Used by