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.
- AI-generated: a genuinely novel statement formulated by AI, with no source for the claim itself.
These labels describe origin, not correctness: citations and verification chips remain separate evidence.
Covariance operator and reproducing-kernel Hilbert space of a Banach Gaussian
Definition
Assume AC. Let be a centered Borel Gaussian random element in a real separable Banach space , with law . For define The Cameron–Martin reproducing-kernel Hilbert space is the completion of in the inner product induced by , where . The canonical map sends the class of to and extends continuously to an injective map ; we identify with its image in and use the completed Hilbert norm . The preceding construction is well-defined by The Banach Gaussian covariance and RKHS construction is well-defined ↗.
The term RKHS here denotes this covariance-induced Hilbert space of shifts inside ; it is not the space of all continuous functions on an arbitrary index set.
Depends on
Used by
- Concentration function of a Banach Gaussian prior Definition
- A rank-one finite-dimensional Gaussian process is a tight Banach prior Example
- The Cameron–Martin unit ball is compact in the Banach norm Lemma
- Borell–Sudakov–Tsirelson expansion for a Banach Gaussian law Theorem
- Cameron–Martin translation formula for a separable Banach Gaussian Theorem
- Gaussian concentration-function sieve bounds Theorem
- Gaussian-process posterior contraction in density and regression models Theorem
- The Banach Gaussian covariance and RKHS construction is well-defined Theorem
- The support of a Banach Gaussian is the closure of its RKHS Theorem
Dependency tree · two levels
37 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)
- A. Lunardi, M. Miranda, and D. Pallara, Infinite Dimensional Analysis (standard reference, not scraped)