Reproducing kernel Hilbert spaces of Gaussian priors

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dc.creatorvan der Vaart, A. W.
dc.creatorvan Zanten, J. H.
dc.date2008-05-21
dc.date.accessioned2026-07-07T12:19:09Z
dc.date.available2026-07-07T12:19:09Z
dc.descriptionWe review definitions and properties of reproducing kernel Hilbert spaces attached to Gaussian variables and processes, with a view to applications in nonparametric Bayesian statistics using Gaussian priors. The rate of contraction of posterior distributions based on Gaussian priors can be described through a concentration function that is expressed in the reproducing Hilbert space. Absolute continuity of Gaussian measures and concentration inequalities play an important role in understanding and deriving this result. Series expansions of Gaussian variables and transformations of their reproducing kernel Hilbert spaces under linear maps are useful tools to compute the concentration function.
dc.descriptionPublished in at http://dx.doi.org/10.1214/074921708000000156 the IMS Collections (http://www.imstat.org/publications/imscollections.htm) by the Institute of Mathematical Statistics (http://www.imstat.org)
dc.identifierhttps://arxiv.org/abs/0805.3252
dc.identifierhttp://arxiv.org/abs/0805.3252
dc.identifierIMS Collections 2008, Vol. 3, 200-222
dc.identifierdoi:10.1214/074921708000000156
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/212650
dc.subjectFunctional Analysis
dc.subjectOperator Algebras
dc.subjectStatistics Theory
dc.subject60G15, 62G05 (Primary)
dc.titleReproducing kernel Hilbert spaces of Gaussian priors
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