Alphabeta Math
CounterexampleConstruction: AI-generatedVerification: AI-generatedPipeline-generatedprecheck passverified 2026-10-22 (gpt-6.1-sol)
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A point-mass Gaussian prior excluding the truth prevents contraction

Statement refuted

Assume AC and Countable Choice. In every positive-noise Gaussian sequence inverse experiment, every proper centered Gaussian prior on ℓ2 yields posterior contraction at some rate rϵ→0 about every truth θ†∈ℓ2, including truths for which the prior gives zero mass to every ball of radius less than 1 centered at θ†.

Facts & Assumptions

Given: AC and Countable Choice; the positive-noise sequence inverse experiment; its standard basis vector e1=(1,0,0,…); and the posterior contraction definition.

[A1]

AC and Countable Choice are assumed by the Gaussian sequence model and its product-law theorem. The Axiom of Choice, The Axiom of Countable Choice (ACω)

[F1]

The data coordinates are Yj=sjθj+ϵξj with positive sj and independent standard-normal noise. Gaussian sequence inverse model

[F2]

The conjugate contraction theorem assumes τj>0 and ∑jτj2<∞; its conclusion is not stated for every Gaussian prior without those hypotheses. Exact posterior second moment and contraction bound for a Gaussian sequence inverse problem

[F3]

The law N(0,0) is the point mass δ0. Standard normal and normal laws

[F4]

A nondegenerate finite-dimensional Gaussian with covariance ϵ2I has density proportional to exp⁡(−∥y−m∥22/(2ϵ2)). A positive-definite multivariate normal law has its Gaussian density

[F5]

Finite measures agreeing on a generating cylinder π-system and on the whole space agree on the generated product Borel sigma-algebra. Finite measures agreeing on a generating pi-system and on the whole space are equal

[F7]

The posterior contraction condition requires the expected posterior mass outside every diverging multiple of a rate to tend to zero under the fixed-truth data law. Posterior contraction rate in a Gaussian sequence inverse experiment

Counterexample

Given: Let the prior be Π0=δ0 on ℓ2, let the truth be θ†=e1, and let s1>0 and ϵ>0.

1.1A1F1F3givenconstruct

The prior δ0=N(0,0) is a proper centered Gaussian law. Under its prior-predictive joint law, θ=0 almost surely, so for every Borel A⊆ℓ2 and product-Borel B⊆RN>0, P(θ∈A,Y∈B)=1A(0)P0ϵ(Y∈B)=∫Bδ0(A) P0ϵ(dy). Thus the constant kernel y↦δ0 is a posterior version. The prior assigns zero mass to every ball centered at e1 of radius less than 1.

2.1A1F1F4F5F6givenstep 1.1algebra

Under the fixed truth e1, only the first data coordinate shifts. Relative to the prior-predictive data law, the one-coordinate normal density ratio is L(y)=exp⁡ ⁣(s1y1ϵ2−s122ϵ2), and it integrates to 1 under the prior-predictive first-coordinate law. It is positive and finite for every y by [F4]–[F6]. All remaining coordinates have identical independent laws. The two full product-Borel data laws therefore agree as finite measures on the generating cylinder π-system after weighting by L; [F5] extends the identity to all data events. Since L>0, the laws are equivalent, so the posterior version δ0 remains the same almost surely under the truth law.

3.1F2F7givenstep 1.1step 2.1algebra∎

For any candidate rate rϵ→0, take Mϵ=rϵ−1/2 for sufficiently small ϵ. Then Mϵ→∞ and Mϵrϵ=rϵ<1 eventually. The posterior is concentrated at 0, whose distance from e1 is 1, so its mass outside the radius-Mϵrϵ ball is exactly 1 under the truth law. This violates [F7] and refutes the Statement. The example lies outside the positive-coordinate-variance hypotheses in [F2].

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