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Book synopsis

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.

Book details

Book category Books in English Mathematics & science Mathematics Probability & statistics

• Full title: Bayesian Inference in Wavelet-Based Models
• Author: Peter, Miller
• Language: English
• Binding: Paperback
• Number of pages: 396
• EAN: 9780387988856
• ISBN: 0387988858
• ID: 04067086
• Publisher: Springer-Verlag New York Inc.
• Weight: 1280 g
• Dimensions: 235 × 155 × 23 mm
• Date of publishing: 22. June 1999

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