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ExampleConstruction: AI-generatedVerification: AI-adaptedPipeline-generatedjudge pass (gpt-6-luna)verified 2026-10-22 (gpt-6.1-sol)
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A trace-class Gaussian with full support and a proper Cameron–Martin space

Example

Assume the Axiom of Choice and countable choice. In H=ℓ2(N,R), use the positively indexed complete coordinate basis (ej)j≥1: ej has value 1 at coordinate j−1 and 0 at every other coordinate. Throughout this example, xj=⟨x,ej⟩ denotes the coefficient in this positively indexed basis. Let ξj be independent N(0,1) variables and set W=∑j≥1j−1ξjej. Take the parameter and data spaces both to be H with their Borel sigma-algebras, the admissible truth set and forward domain to be U=D=H, and the forward map to be the identity. The law μ of W is a centered Borel Gaussian prior on D. Its covariance is diagonal with positive eigenvalues j−2, whose finite sum ∑j≥1j−2 makes it trace class, meaning that the sum of these nonnegative eigenvalues is finite. Its covariance reproducing-kernel Hilbert space is the dense proper subspace of H {x∈ℓ2:∑j≥1j2xj2<∞}, which is not all of H. The topological support of μ, defined as the set of points every open neighborhood of which has positive μ-measure, is all of H.

Example references

Facts & Assumptions

Given: AC and countable choice, a probability space carrying independent N(0,1) variables (ξj)j≥1, H=ℓ2 with standard basis (ej), and X as in the Example.

[A1]

AC is required by the Borel Gaussian, covariance/RKHS, tightness, and support interfaces. The Axiom of Choice

[A2]

Countable choice is required by the Gaussian-law, Hilbert Riesz, and support interfaces. The Axiom of Countable Choice (ACω)

[F1]

For a separable Hilbert space with basis ej, weights wj=1, and λj=j−2, if ∑jλj<∞ then the series converges in the Hilbert norm and its law is a centered Borel Gaussian with these covariance eigenvalues. Weighted Hilbert Gaussian series and prior regularity

[F2]

A Borel probability on a parameter space is a prior on D only when it assigns D probability one. A Borel prior concentrated on a forward-map domain

[F3]

For a centered Borel Gaussian, the covariance is Cℓ=E[ℓ(X)X] and the covariance RKHS completes the quotient of H∗ by the nullspace of Q(ℓ,m)=E[ℓ(X)m(X)]. Covariance operator and reproducing-kernel Hilbert space of a Banach Gaussian

[F4]

Under AC, each covariance Cℓ exists as a Bochner integral and the completed covariance map embeds continuously and injectively into the Banach space. The Banach Gaussian covariance and RKHS construction is well-defined

[F5]

Under countable choice, every bounded linear functional on a Hilbert space is represented uniquely by inner product with a vector, with equal dual and vector norms. Riesz representation for Hilbert spaces

[F6]

Under AC, every Borel probability on a Polish space is tight. Every borel probability on a polish space is tight

[F7]

For a centered tight Borel Gaussian on a real separable Banach space, assuming AC and countable choice, its topological support is the Banach closure of its covariance RKHS. The support of a Banach Gaussian is the closure of its RKHS

[F8]

A bounded linear map commutes with the Bochner integral over every measurable set. Bounded linear maps commute with Bochner integration

[F9]

The real square-summable sequence space with its canonical inner product is complete and hence Hilbert. The real square-summable sequence space is complete

[F10]

Reindexing the coordinate vectors by j↦j−1 for j≥1, the coordinate vectors of real ℓ2 form a complete orthonormal basis and their partial sums converge in norm. Coordinate vectors form the Hilbert basis of real ell-two

[F11]

A Polish space is separable and completely metrizable. Polish spaces are separable completely metrizable spaces

Verification

Given: The series, basis, probability space, and axioms in the Example.

1.1givenalgebra

Group the positive terms by dyadic blocks. For each m≥0, there are 2m integers j with 2m≤j<2m+1, and each has j−2≤2−2m. Hence ∑j≥1j−2≤∑m≥02m2−2m=∑m≥02−m<∞.

2.1A1A2F1F2F10step 1.1givenconstruct

By [F10], the coordinate vectors are a complete orthonormal basis of H. Apply [F1] using step 1.1 under the stated AC and countable-choice assumptions [A1]–[A2]. This gives the centered Borel Gaussian law μ on H, with covariance operator eigenvalues j−2. The domain is D=H and the identity map is measurable, so μ(D)=1 and [F2] makes μ a prior on D.

3.1A1A2F1F3F4F5F8step 2.1givenalgebra

By [F5], every ℓ∈H∗ has the form ℓz(x)=⟨x,z⟩ for a unique z∈ℓ2. The covariance operator supplied by [F1] in step 2.1 is diagonal under this Riesz identification. By the covariance definition [F3] and commutation of bounded functionals with the Bochner integral [F8], Q(ℓz,ℓy)=ℓy(Cℓz)=∑jj−2zjyj; this sum converges absolutely by Cauchy–Schwarz and j−2≤1. The well-definedness theorem [F4] supplies Cℓz and the quotient completion. In particular, applying the coordinate functional mj(x)=⟨x,ej⟩ commutes with the Bochner integral by [F8], so ⟨Cℓz,ej⟩=mj(Cℓz)=Q(ℓz,mj)=j−2zj. Thus the canonical covariance map sends z to the vector with coordinates xj=j−2zj. Its quotient norm satisfies ∥z∥Q2=Q(ℓz,ℓz)=∑jj−2zj2=∑jj2xj2. The nullspace is zero because every j−2>0. Finite truncations of Riesz vectors are dense in the quotient completion since their weighted-square tails tend to zero; conversely, for any x∈ℓ2 with ∑jj2xj2<∞, the truncations of zj=j2xj converge in that norm and their images converge to x in ℓ2. Therefore the covariance RKHS is exactly the displayed weighted subspace.

3.2A1A2F6F9F10F11step 2.1givenconstructalgebra

By [F9], ℓ2 is a complete real Hilbert space, and [F10] gives a countable complete orthonormal basis. Its finite real linear span is dense; approximating each finite list of real coefficients by rationals shows that finite rational combinations are dense as well. They form a countable set, so [F11] makes ℓ2 Polish. Step 2.1 gives a Borel probability μ on this space; [F6] makes μ tight under AC [A1].

4.1F10step 1.1step 3.1algebra

By [F10], finite-support sequences are dense in ℓ2; each lies in the RKHS identified in step 3.1, so the RKHS is dense. The vector x=(1/j)j≥1 belongs to ℓ2 by step 1.1 but not to the RKHS, since ∑jj2(1/j)2=∑j1=∞. Therefore the RKHS is proper.

5.1A1A2F7step 2.1step 3.2step 4.1∎

The hypotheses of [F7] hold: step 2.1 gives a centered Borel Gaussian, step 3.2 gives tightness, and AC and countable choice [A1]–[A2] are assumed. By step 4.1 the covariance RKHS has closure H, so [F7] gives supp⁡μ=H.

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