How statement and proof provenance work
The first chip identifies the source of the statement or construction; the second identifies the source of its local proof or verification.
- Literature-sourced: the exact statement appears in a cited source; only wording and notation differ.
- AI-adapted: a semantically identical restatement of literature-sourced material, modulo indexing, notation, and boundary cases adopted by the library.
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These labels describe origin, not correctness: citations and verification chips remain separate evidence.
Centered Borel Gaussian random element on a separable Banach space
Definition
Let be a probability space and let be a real separable Banach space with its norm Borel sigma-algebra. A map is a centered Borel Gaussian random element if it is Borel-measurable and, for every , the real random variable has a centered (possibly zero-variance) normal law. Its law is the Borel probability . When the word tight is used, it means that for every there is a norm-compact with .
This definition concerns a random element in . A Gaussian process on an arbitrary index set with only a cylinder-space law does not satisfy it unless a Borel -valued realization is separately constructed.
Depends on
- A measurable function between measurable spaces
- Borel probability law on a polish space
- Polish spaces are separable completely metrizable spaces
- Separability: the existence of an at most countable dense subset
- The dual space X^* of a normed space and its dual norm
- Standard normal and normal laws
- The Axiom of Choice
Used by
- Covariance operator and reproducing-kernel Hilbert space of a Banach Gaussian Definition
- A rank-one finite-dimensional Gaussian process is a tight Banach prior Example
- An iid Gaussian cylinder process is not an ℓ²-valued prior Example
- Uncorrelated jointly Gaussian Banach random elements are independent Lemma
- Borell concentration for the norm of a separable Banach Gaussian Theorem
- Fernique integrability for a separable Banach Gaussian law Theorem
- Gaussian-process posterior contraction in density and regression models Theorem
- The Banach Gaussian covariance and RKHS construction is well-defined Theorem
Dependency tree · two levels
24 results within two dependency steps of this one, each drawn at its shortest distance from it. An arrow runs from a result to what uses it, so the chart reads left to right and ends at this result, which carries a heavier outline. Every node is a link to that result. Click elsewhere on the chart to enlarge it.
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)