Bayesian inference in wavelet-based models /
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 introduct...
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| Other Authors: | , |
| Format: | eBook |
| Language: | English |
| Published: |
New York :
Springer,
[1999]
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| Series: | Lecture notes in statistics (Springer-Verlag) ;
v. 141. |
| Subjects: | |
| Online Access: | Connect to the full text of this electronic book |
| Summary: | 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. |
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| Item Description: | Electronic resource. |
| Physical Description: | 1 online resource (xiii, 394 pages) : illustrations. |
| Format: | Master and use copy. Digital master created according to Benchmark for Faithful Digital Reproductions of Monographs and Serials, Version 1. Digital Library Federation, December 2002. |
| Bibliography: | Includes bibliographical references. |
| ISBN: | 9781461205678 (electronic bk.) 1461205670 (electronic bk.) |