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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 , use the positively indexed complete coordinate basis : has value at coordinate and at every other coordinate. Throughout this example, denotes the coefficient in this positively indexed basis. Let be independent variables and set Take the parameter and data spaces both to be with their Borel sigma-algebras, the admissible truth set and forward domain to be , and the forward map to be the identity. The law of is a centered Borel Gaussian prior on . Its covariance is diagonal with positive eigenvalues , whose finite sum 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 which is not all of . The topological support of , defined as the set of points every open neighborhood of which has positive -measure, is all of .
Example references
summable weighted Gaussian series converge in the selected Hilbert norm
a prior requires full measure on the forward-map domain
the parameter and data spaces, admissible truth set, and forward map
the covariance quotient completion defining the covariance RKHS
well-definedness of the covariance and RKHS construction
bounded linear coordinates commute with Bochner integration
under countable choice, bounded Hilbert functionals are inner products
Borel probabilities on Polish spaces are tight under AC
real square-summable sequences form a complete Hilbert space
the coordinate vectors form a complete orthonormal basis of real ell-two
the support of a centered tight Banach Gaussian is the Banach closure of its RKHS
Dashti and Stuart, §2.4, Theorem 2.10 and Remarks 2.11, covariance convention
Facts & Assumptions
Given: AC and countable choice, a probability space carrying independent variables , with standard basis , and as in the Example.
AC is required by the Borel Gaussian, covariance/RKHS, tightness, and support interfaces. The Axiom of Choice
Countable choice is required by the Gaussian-law, Hilbert Riesz, and support interfaces. The Axiom of Countable Choice ()
For a separable Hilbert space with basis , weights , and , if 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
A Borel probability on a parameter space is a prior on only when it assigns probability one. A Borel prior concentrated on a forward-map domain
For a centered Borel Gaussian, the covariance is and the covariance RKHS completes the quotient of by the nullspace of . Covariance operator and reproducing-kernel Hilbert space of a Banach Gaussian
Under AC, each covariance 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
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
Under AC, every Borel probability on a Polish space is tight. Every borel probability on a polish space is tight
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
A bounded linear map commutes with the Bochner integral over every measurable set. Bounded linear maps commute with Bochner integration
The real square-summable sequence space with its canonical inner product is complete and hence Hilbert. The real square-summable sequence space is complete
Reindexing the coordinate vectors by for , the coordinate vectors of real form a complete orthonormal basis and their partial sums converge in norm. Coordinate vectors form the Hilbert basis of real ell-two
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.
Group the positive terms by dyadic blocks. For each , there are integers with , and each has . Hence .
By [F10], the coordinate vectors are a complete orthonormal basis of . Apply [F1] using step 1.1 under the stated AC and countable-choice assumptions [A1]–[A2]. This gives the centered Borel Gaussian law on , with covariance operator eigenvalues . The domain is and the identity map is measurable, so and [F2] makes a prior on .
By [F5], every has the form for a unique . 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], ; this sum converges absolutely by Cauchy–Schwarz and . The well-definedness theorem [F4] supplies and the quotient completion. In particular, applying the coordinate functional commutes with the Bochner integral by [F8], so . Thus the canonical covariance map sends to the vector with coordinates . Its quotient norm satisfies . The nullspace is zero because every . Finite truncations of Riesz vectors are dense in the quotient completion since their weighted-square tails tend to zero; conversely, for any with , the truncations of converge in that norm and their images converge to in . Therefore the covariance RKHS is exactly the displayed weighted subspace.
By [F9], 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 Polish. Step 2.1 gives a Borel probability on this space; [F6] makes tight under AC [A1].
By [F10], finite-support sequences are dense in ; each lies in the RKHS identified in step 3.1, so the RKHS is dense. The vector belongs to by step 1.1 but not to the RKHS, since . Therefore the RKHS is proper.
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 , so [F7] gives .
Depends on
- Inverse-problem models and solution classes
- Weighted Hilbert Gaussian series and prior regularity
- A Borel prior concentrated on a forward-map domain
- Covariance operator and reproducing-kernel Hilbert space of a Banach Gaussian
- The Banach Gaussian covariance and RKHS construction is well-defined
- Bounded linear maps commute with Bochner integration
- The support of a Banach Gaussian is the closure of its RKHS
- Every borel probability on a polish space is tight
- The real square-summable sequence space is complete
- Coordinate vectors form the Hilbert basis of real ell-two
- Separability: the existence of an at most countable dense subset
- Polish spaces are separable completely metrizable spaces
- Riesz representation for Hilbert spaces
- The Axiom of Choice
- The Axiom of Countable Choice ($\mathrm{AC}_\omega$)
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Sources
- M. Dashti and A. M. Stuart, The Bayesian Approach to Inverse Problems (arXiv:1302.6989) (standard reference, not scraped)
- E. Giné and R. Nickl, Mathematical Foundations of Infinite-Dimensional Statistical Models (Cambridge University Press, 2016) (standard reference, not scraped)