Bayesian Inference in Wavelet-Based Models (Paperback, Softcover reprint of the original 1st ed. 1999)

This volume provides a thorough introduction and reference for any researcher who is interested in Bayesian inference for wavelet-based models, but is not necessarily an expert in either. To achieve this goal the book starts with an extensive introductory chapter providing a self-contained introduction to the use of wavelet decompositions and the relation to Bayesian inference. The remaining papers in this volume are divided into six parts: independent prior modeling; decision theoretic aspects; dependent prior modeling; spatial models using bivariate wavelet bases; empirical Bayes approaches; and case studies. Chapters are written by experts who published the original research papers establishing the use of wavelet-based models in Bayesian inference. Peter Müller is Associate Professor and Brani Vidakovic is Assistant Professor of Statistics at Duke University.

General

Imprint:	Springer-Verlag New York
Country of origin:	United States
Series:	Lecture Notes in Statistics, 141
Release date:	2001
First published:	1999
Editors:	Peter Muller • Brani Vidakovic
Dimensions:	235 x 155 x 21mm (L x W x T)
Format:	Paperback
Pages:	396
Edition:	Softcover reprint of the original 1st ed. 1999
ISBN-13:	978-0-387-98885-6
Categories:	Books > Science & Mathematics > Mathematics > Probability & statistics Promotions
LSN:	0-387-98885-8
Barcode:	9780387988856

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