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Credible sets and frequentist coverage — Examples and Counterexamples

1 · Prerequisites

2 · Logical flowchart

3 · Definitions, theorems and proofs

None yet.

4 · Examples, counterexamples and false statements

ExampleConstruction: AI-generatedVerification: AI-generatedjudge pass (gpt-6-luna)verified 2026-10-22 (gpt-6.1-sol)Open item page →

A proper Gaussian prior yields a credible interval with undercoverage

Example

Assume AC. Observe Y=θ+Z with fixed truth θ∈R, Z∼N(0,1), and proper prior θ∼N(0,1). Let ϕ(x)=e−x2/2/2π and Φ(y)=∫−∞yϕ(x) dx, let z>0 be the unique number satisfying Φ(z)=0.975, and put r=z/2. The posterior is N(Y/2,1/2), so the central interval I(Y)=[Y/2−r,Y/2+r] has posterior mass exactly 0.95 for every Y. At the fixed truth θ∗=2r+1, its frequentist coverage is P{1≤Z≤4r+1}<0.95.

Facts & Assumptions

Given: AC and the one-dimensional normal likelihood and proper normal prior in the Example, with z=Φ−1(0.975) and r=z/2.

[A1]

AC states that every family of nonempty sets has a choice function. The Axiom of Choice

[F1]

Posterior credibility and frequentist coverage: posterior mass is computed under the data-indexed posterior kernel, while frequentist coverage is the fixed-truth sampling probability of set membership.

[F2]

Standard normal and normal laws: N(0,1) has a positive symmetric standard normal density, and N(m,σ2) is its affine image under x↦m+σx.

[F3]

If f is continuous on a closed interval [a,b], then every value between f(a) and f(b) equals f(c) for some c∈[a,b]. Intermediate value theorem, by bisection with a canonical left-half rule: a continuous function on [a,b] takes every value between f(a) and f(b)

Verification

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

1.1A1F2F3givenchoose

Under [A1], the standard normal probability law of [F2] is supplied. Its density ϕ is positive and symmetric and integrates to one, so Φ is continuous and strictly increasing, with Φ(0)=1/2 and Φ(y)→1 as y→∞. Choose B>0 with Φ(B)>0.975. By [F3] on [0,B], there is a unique z>0 with Φ(z)=0.975.

1.2A1F2givenalgebra

The prior density times likelihood is proportional to exp⁡{−12[θ2+(Y−θ)2]}=exp⁡{−(θ−Y/2)2} times a factor independent of θ. Normalization gives posterior law N(Y/2,1/2).

1.3F1F2givenalgebra

Under the fixed truth θ∗=2r+1, write Y=θ∗+Z. The event θ∗∈I(Y) is equivalent to ∣Z−θ∗∣≤2r, hence to 1≤Z≤4r+1.

2.1F1givenstep 1.1algebra

The interval membership graph is {(Y,θ):∣θ−Y/2∣≤r}, the inverse image of the closed interval [−r,r] under a continuous map. It is closed, hence Borel, so the sampling membership event is measurable.

2.2F1F2givenstep 1.1step 1.2algebra

Under that posterior, (θ−Y/2)/1/2 is standard normal. The interval radius r=z/2 therefore gives posterior mass Φ(z)−Φ(−z)=0.975−0.025=0.95 for every observed Y.

3.1F2step 2.2step 1.3algebra∎

Since the standard normal density is positive and symmetric, P(1≤Z≤4r+1)<P(Z≥0)=1/2<0.95. Thus the interval has posterior mass 0.95 but frequentist coverage below 0.95 at θ∗.

Remarks

A separate one-observation Bernoulli example with a uniform prior has zero coverage at the truth θ=0.005; that conclusion is specific to its Bernoulli likelihood and prior. The Gaussian calculation above uses a different proper prior and sampling law.

CounterexampleConstruction: Literature-sourcedVerification: AI-adaptedjudge pass (gpt-6-luna)verified 2026-10-22 (gpt-6.1-sol)Open item page →

Sparse-gap truths defeat uniform coverage over a hyperrectangle

Statement refuted

Assume AC. Fix p≥0, C≥1, β>0, finite A>β, 0<M≤(1984C8)−1, and 0<γ<1. For n≥3, observe Xi=κiθi+n−1/2Zi in the Gaussian sequence experiment, where θ∈ℓ2(N+,R) is fixed, C−2i−2p≤κi2≤C2i−2p, and the Zi are independent N(0,1). Define Θβ(M)={θ∈ℓ2:sup⁡i≥1i1+2βθi2≤M}. For 0<α≤A, use the product prior Πα=⨂i≥1N(0,i−1−2α) and its product Gaussian posterior with coordinate mean θ^n,α,i(x)=nκixi/(i1+2α+nκi2) and variance (i1+2α+nκi2)−1. Select α^n as a Borel global maximizer over [0,A] of ℓn(α;x)=−12∑i≥1[log⁡(1+nκi2i1+2α)−n2κi2xi2i1+2α+nκi2], Assume the score series is finite on a Borel full-measure data domain for the truths considered and is upper semicontinuous in α there. For α^n>0, let rn,γ(α^n) be the 1−γ posterior quantile of ∥θ−θ^n,α^n(X)∥2, and let C^n(L) be the ball centered at θ^n,α^n(X) with radius Lrn,γ(α^n). Set C^n(L)=ℓ2 if α^n=0. There exists a finite fixed L≥1 for which lim inf⁡n→∞inf⁡θ∈Θβ(M)Pθ(n){θ∈C^n(L)}≥1−γ

Facts & Assumptions

Given: AC and the model, prior, selector, p,C,β,A,M,γ in the Statement refuted.

[A1]

AC supplies the countable Gaussian product construction used by the empirical-Bayes procedure. The Axiom of Choice

[F1]

Θβ(M)={θ∈ℓ2:sup⁡i≥1i1+2βθi2≤M}. Hyperrectangle classes in a Gaussian sequence model

[F2]

θ∈Θpt(L0,N0,ρ) only if ∑i=N∞θi2≤L0∑i=N⌊ρN⌋θi2 for every integer N≥N0. Polished-tail class for a Gaussian sequence

[F3]

For the sparse-gap construction below, every deterministic nonnegative inflation sequence satisfying Lnj≤KLM ρj(1+2p)/(8+8β+8p) eventually, for some finite KL>0, has Pθ0(nj){θ0∈C^nj(Lnj)}→0. Sparse-gap truth causes empirical-Bayes credible-set undercoverage

[F4]

For each fixed A,L0,N0,ρ, there is a finite inflation constant L for which coverage tends to one uniformly over Θpt(L0,N0,ρ) with θ1=0. Conservative coverage of adaptive empirical-Bayes credible balls on polished tails

[F5]

For α^n>0, the procedure uses the posterior-mean-centered ℓ2 ball with radius L times the 1−γ posterior quantile; its zero-selector branch is the whole space. Empirical-Bayes credible-ball procedure in the inverse Gaussian sequence model

[F6]

At a fixed truth, frequentist coverage is the sampling probability of the membership event {θ∈Cn(Y)}. Posterior credibility and frequentist coverage

Counterexample

Put s=1+2β+2p, ρj=2j, n1=2, and nj+1=⌈(2ρj+12)snj⌉. Set Nj=nj1/s and define the fixed truth by θ0,i2={0,Nj/ρj<i≤Nj for some j,0,2Nj<i≤ρjNj for some j,Mi−1−2β,otherwise. For every fixed L≥1, this truth belongs to Θβ(M), has θ0,1=0, is outside every fixed polished-tail class, and has subsequence coverage tending to zero.

Given: The hypotheses in the Statement refuted and the construction above.

1.1F1givenalgebra

Every squared coordinate is either zero or Mi−1−2β, so sup⁡ii1+2βθ0,i2≤M and ∑iθ0,i2≤M∑ii−1−2β<∞. Also N1=21/s∈(1,2) and ρ1=2, hence N1/ρ1<1≤N1 and the first zero gap contains i=1. Thus θ0∈Θβ(M) and θ0,1=0.

2.1F2givenstep 1.1algebra

Fix any polished-tail parameters L0≥1, integer N0≥1, and ρ∗≥2. The recursion gives Nj/ρj≥2ρjNj−1>ρj−1Nj−1, so every earlier gap ends below Nj/ρj. Also Nj′=⌊2Nj⌋+1≤2Nj+1; because Nj→∞ and ρj→∞, for all sufficiently large j we have Nj′≥N0 and ρ∗(2Nj+1)≤ρjNj, whence ⌊ρ∗Nj′⌋≤ρjNj. Since Nj′>2Nj, the block [Nj′,⌊ρ∗Nj′⌋] lies in the current zero gap (2Nj,ρjNj]. For all sufficiently large j, ij=⌊ρjNj⌋+1≤ρjNj+1<4ρjNj≤Nj+1/ρj+1, so ij is before the next first gap; applying the same separation at later indices puts all still later gaps farther right. Since ρjNj>2Nj+1≥Nj′ eventually, ij>Nj′; earlier gaps end below Nj/ρj<ij. Hence ij lies outside every zero gap and is in the tail from Nj′, with θ0,ij2=Mij−1−2β>0. The block sum is zero while that tail is positive, violating [F2]. Thus θ0 is outside every fixed polished-tail class.

2.2A1F3F5F6givenstep 1.1algebra

For a fixed L≥1, take Lnj=L and KL=L/M. Since (1+2p)/(8+8β+8p)>0 and ρj≥1, we have Lnj=L≤KLM ρj(1+2p)/(8+8β+8p) for every j. Applying [F3] gives Pθ0(nj){θ0∈C^nj(L)}→0. By [F6] this is the frequentist coverage at the fixed truth; because θ0∈Θβ(M) by step 1.1, the infimum coverage over that hyperrectangle cannot have lower limit at least 1−γ.

3.1F2F4step 2.1step 2.2algebra∎

The positive coverage statement [F4] has the additional polished-tail hypothesis, which fails for θ0 by step 2.1. Thus the sparse-gap undercoverage does not contradict the polished-tail theorem; it refutes only the broader fixed-inflation claim over the full hyperrectangle.

Sources