Fuzzy sets in nonparametric Bayes regression

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dc.creatorAngers, Jean-François
dc.creatorDelampady, Mohan
dc.date2008-05-21
dc.date.accessioned2026-07-07T12:19:07Z
dc.date.available2026-07-07T12:19:07Z
dc.descriptionA simple Bayesian approach to nonparametric regression is described using fuzzy sets and membership functions. Membership functions are interpreted as likelihood functions for the unknown regression function, so that with the help of a reference prior they can be transformed to prior density functions. The unknown regression function is decomposed into wavelets and a hierarchical Bayesian approach is employed for making inferences on the resulting wavelet coefficients.
dc.descriptionPublished in at http://dx.doi.org/10.1214/074921708000000084 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/0805.3209
dc.identifierhttp://arxiv.org/abs/0805.3209
dc.identifierIMS Collections 2008, Vol. 3, 89-104
dc.identifierdoi:10.1214/074921708000000084
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/212644
dc.subjectMethodology
dc.subjectLogic
dc.subjectStatistics Theory
dc.subject62G08 (Primary) 62A15, 62F15 (Secondary)
dc.titleFuzzy sets in nonparametric Bayes regression
dc.typetext

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