Alphabeta Math
ExampleConstruction: AI-generatedVerification: AI-generatedPipeline-generatedverified 2026-10-15 (gpt-6-luna, gpt-6.1-sol, and delegated Codex review/repair agents; see completed run evidence)
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.

  • Literature-sourced: the exact statement appears in a cited source; only wording and notation differ.
  • AI-adapted: a semantically identical restatement of literature-sourced material, modulo indexing, notation, and boundary cases adopted by the library.
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These labels describe origin, not correctness: citations and verification chips remain separate evidence.

Specific Relative Entropy of a Poisson Law with Changed Intensity

Example

For intensity z>0, let P=Qz be the translation-invariant Poisson law from Poisson Configuration Reference, compared with the unit-intensity reference Q=Q1. Its specific relative entropy is I(Qz)=1−z+zlog⁡z. The parameter z is a mathematical intensity relative to the chosen reference; no physical preparation is asserted.

Facts & Assumptions

Given: AC, d≥1, z>0, and P=Qz with reference Q=Q1.

[F1]

The law Qλ is invariant under every translation, its cell count is Poisson with mean λ, conditional positions have the same uniform law for all λ, and disjoint cells are independent (Poisson Configuration Reference, Poisson Reference Counts and Translation Invariance).

[F2]

Finite-region relative entropy is computed from a Radon–Nikodym density, and the specific entropy is the normalized cube limit (Relative Entropy of Probability Measures, Specific Relative Entropy Density).

[F4]

The class Pstat1(Ω) consists exactly of laws invariant under every Tx with finite EPNC0. Such laws integrate every local tame observable (Local-Tame Topology).

Verification

Given: The hypotheses and the laws in Facts & Assumptions.

1.1F1algebra

In one unit cell, the conditional distribution of point positions given the count is identical for Qz and Q1. The ratio of their count probabilities at n is e−zzn/n!e−1/n!=e1−zzn. Thus this is the Radon–Nikodym density on that cell.

2.1F1step 1.1algebra

The cube Λm=[0,m)d is the disjoint union of md unit cells. Independence in [F1] makes the finite-cube density the product of the cell densities: hm(ω)=e(1−z)mdzNΛm(ω).

3.1F1F2F3step 2.1algebra

The count NΛm is Poisson with mean zmd by [F1]. Directly from its mass function and the exponential series, EPNΛm=e−zmd∑n≥1n(zmd)nn!=zmde−zmd∑k≥0(zmd)kk!=zmd. Therefore [F2] gives D(PΛm∥QΛm)md=EPlog⁡hmmd=1−z+zlog⁡z for every positive integer m.

4.1F1F2F4step 3.1∎

By [F1], Qz is invariant under every translation, and step 3.1 with m=1 gives EQzNC0=z<∞. Thus Qz∈Pstat1(Ω) by [F4], which verifies the domain hypothesis of the specific-entropy theorem in [F2]. Its integer-cube supremum formula applied to step 3.1 yields I(Qz)=1−z+zlog⁡z and identifies this value with the real-side cube limit. At z=1 the value is zero, the reference-law baseline.

Depends on

Dependency tree · 2 levels