Fuzzy sets in nonparametric Bayes regression

A 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 functio...

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Bibliographic Details
Published in:arXiv.org 2008-05
Main Authors: Angers, Jean-François, Mohan Delampady
Format: Article
Language:English
Subjects:
Bayesian analysis
Fuzzy sets
Regression
Wavelet analysis
Online Access:Get full text
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Summary:A 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.
ISSN:2331-8422
DOI:10.48550/arxiv.0805.3209