Alphabeta Math
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A separating event forces relative entropy

Statement

For finite distributions P,Q and a vector X∈[0,1]^m, if P·X≤0.06 and Q·X≥0.94, then D(Q∥P)≥1 (with +∞ allowed).

Facts & Assumptions

Given: Probability vectors P,Q on [m] and X∈[0,1]m with PXT≤0.06 and QXT≥0.94.

[F1]

Finite distributions, entropy, divergence, and stochastic matrices: D(p∥q)=∑i:pi>0piln⁡(pi/qi), with value +∞ if pi>0=qi for some i.

[F2]

The logarithm tangent inequality: For every t>0, ln⁡t≤t−1.

Proof

technique · direct
1.1givenF1algebra

If D(Q∥P)=+∞, the result holds. Otherwise form distributions on [m]×{0,1} by Q~(i,1)=QiXi, Q~(i,0)=Qi(1−Xi) and the analogous formulas for P~. Expanding the defining finite sum and summing the common two-outcome kernel gives D(Q~∥P~)=D(Q∥P).

2.1step 1.1F1F2algebra

Group the terms of this divergence according to the second coordinate z. For each z, the group equals qzln⁡(qz/pz)+qzD(Q~(⋅∣z)∥P~(⋅∣z)), where qz=Q~(z) and pz=P~(z); the conditional divergence is nonnegative by applying [F2] to each ratio, and zero-mass cases follow by the stated +∞ convention. Hence D(Q∥P)≥DBer(QXT∥PXT).

3.1givenstep 2.1algebra∎

For 0<q<p<1, the binary divergence K(p,q)=pln⁡(p/q)+(1−p)ln⁡((1−p)/(1−q)) has ∂pK=ln⁡p(1−q)q(1−p)>0 and ∂qK=q−pq(1−q)<0. Therefore the hypotheses imply K(QXT,PXT)≥K(0.94,0.06)=0.88ln⁡(47/3)>1, since 47/3>9>e2. Boundary values follow by limits or give infinite divergence. Combining with step 2.1 proves D(Q∥P)≥1.

Depends on

Used by

Dependency tree · two levels

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Sources