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The support of a Banach Gaussian is the closure of its RKHS
Statement
Assume AC and countable choice. Let be a centered tight Borel Gaussian law on a real separable Banach space , and let be its covariance RKHS. Define to be the set of for which every open neighborhood has positive -measure. Then
Facts & Assumptions
Cameron–Martin translations by are equivalent to (Cameron–Martin translation formula for a separable Banach Gaussian).
Tightness supplies a compact set of positive measure (Every borel probability on a polish space is tight).
Centered multivariate normal vectors are determined by their covariance, and countable norming coordinates determine Borel laws on (Multivariate normal law, including singular covariance, Characteristic function of a multivariate normal law, A countable norming family determines Borel laws).
A point outside a closed linear subspace of a normed space can be separated from it by a continuous linear functional (Relative geometric Hahn–Banach with the exact open, closed, and compact hypotheses, The real dominated-extension principle as an additional hypothesis over ZF).
The product of two copies of exists and its coordinate maps are independent (For sigma-finite factors, the product measure exists, has the rectangle formula, is sigma-finite, and is unique, Independent random elements, Independent random elements are characterized by finite rectangle probabilities).
Under AC, the real dominated-extension theorem supplies the Hahn–Banach principle HB used by geometric separation (Hahn-Banach dominated extension theorem for real vector spaces, The real dominated-extension principle as an additional hypothesis over ZF).
Countable dense sets and rational-radius balls give countable bases for the separable Banach factors; their product Borel sigma-algebra is the product of the factor Borel sigma-algebras. Continuous maps have Borel preimages of Borel sets. (Separability: the existence of an at most countable dense subset, Borel sigma-algebra of a product of second-countable spaces, A continuous map has Borel preimages of Borel sets)
A finite stack of independent multivariate Gaussian vectors is multivariate Gaussian, including singular covariance matrices. (A finite stack of independent Gaussian vectors is jointly Gaussian)
Proof
Given: AC, countable choice, a centered tight Borel Gaussian law on a real separable Banach space and its covariance RKHS .
First, . Fix . By [F2] choose compact with and cover it by finitely many balls of radius . One ball has positive -measure. On let be the independent coordinate copies. By [F5], this product sigma-algebra is the Borel sigma-algebra of ; the maps and are continuous, so and are Borel random elements. The event that both copies lie in has positive probability and implies . Choose a countable norming family using countable choice and HB from [A2]. For any finite subfamily, the coordinate vectors of are independent centered multivariate normals with common covariance ; [F6] makes their stack multivariate Gaussian, so its linear image giving the difference is multivariate Gaussian. The vector for is also multivariate Gaussian, and both vectors have covariance . Thus [F3] makes their finite laws equal. Countable norming-law determination in [F3] gives . Hence , and therefore .
Suppose . By [F4] choose with and . For every , , since . Taking gives , so almost surely. Hence . Because is closed and does not contain , some open neighborhood of is disjoint from and has measure zero. Thus .
For , [F1] gives equivalence of the law of with . Thus its mass on is positive by step 1.1. That mass is , so every lies in the support. Since the support is closed, .
Step 2.1 gives , and step 1.2 gives the reverse inclusion. Therefore the two sets are equal.
Depends on
- Covariance operator and reproducing-kernel Hilbert space of a Banach Gaussian
- Separability: the existence of an at most countable dense subset
- The Banach Gaussian covariance and RKHS construction is well-defined
- Cameron–Martin translation formula for a separable Banach Gaussian
- A separable Banach space has a countable norming family
- A countable norming family determines Borel laws
- Multivariate normal law, including singular covariance
- Characteristic function of a multivariate normal law
- A finite stack of independent Gaussian vectors is jointly Gaussian
- Borel sigma-algebra of a product of second-countable spaces
- A continuous map has Borel preimages of Borel sets
- Every borel probability on a polish space is tight
- For sigma-finite factors, the product measure exists, has the rectangle formula, is sigma-finite, and is unique
- Independent random elements
- Independent random elements are characterized by finite rectangle probabilities
- Relative geometric Hahn–Banach with the exact open, closed, and compact hypotheses
- The Axiom of Choice
- The real dominated-extension principle as an additional hypothesis over ZF
- The Axiom of Countable Choice ($\mathrm{AC}_\omega$)
- Hahn-Banach dominated extension theorem for real vector spaces
Used by
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Sources
- A. W. van der Vaart and J. H. van Zanten, Rates of contraction of posterior distributions based on Gaussian process priors (2008) (standard reference, not scraped)
- M. Hairer, An Introduction to Stochastic PDEs (standard reference, not scraped)