Bayesian Inference in Wavelet-Based Models by Brani Vidakovic (English) Paperbac

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Bayesian Inference in Wavelet-Based Models by Brani Vidakovic, Peter Müller

This book links two of the most active areas of statistics, Bayesian statistics and wavelets.

FORMAT
Paperback LANGUAGE
English CONDITION
Brand New Publisher Description

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 Muller is Associate Professor and Brani Vidakovic is Assistant Professor of Statistics at Duke University.

Notes

This book links two of the most active areas of statistics, Bayesian statistics and wavelets.

Table of Contents

I Introduction.- 1 An Introduction to Wavelets.- 2 Spectral View of Wavelets and Nonlinear Regression.- II Prior Models - Independent Case.- 3 Bayesian Approach to Wavelet Decomposition and Shrinkage.- 4 Some Observations on the Iractability of Certain Multi-Scale Models..- 5 Bayesian Analysis of Change-Point Models.- 6 Prior Elicitation in the Wavelet Domain.- 7 Wavelet Nonparametric Regression Using Basis Averaging.- III Decision Theoretic Wavelet Shrinkage.- 8 An Overview of Wavelet Regularization.- 9 Minimax Restoration and Deconvolution.- 10 Robust Bayesian and Bayesian Decision Theoretic Wavelet Shrinkage.- 11 Best Basis Representations with Prior Statistical Models.- IV Prior Models — Dependent Case.- 12 Modeling Dependence in the Wavelet Domain.- 13 MCMC Methods in Wavelet Shrinkage.- V Spatial Models.- 14 Empirical Bayesian Spatial Prediction Using Wavelets.- 15 Geometrical Priors for Noisefree Wavelet Coefficients in Image Denoising.- 16 Multiscale Hidden Markov Models for Bayesian Image Analysis.- 17 Wavelets for Object Representation and Recognition in Computer Vision.- 18 Bayesian Denoising of Visual Images in the Wavelet Domain.- VI Empirical Bayes.- 19 Empirical Bayes Estimation in Wavelet Nonparametric Regression.- 20 Nonparametric Empirical Bayes Estimation via Wavelets.- VII Case Studies.- 21 Multiresolution Wavelet Analyses in Hierarchical Bayesian Turbulence Models.- 22 Low Dimensional Turbulent Transport Mechanics Near the Forest-Atmosphere Interface.- 23 Latent Structure Analyses of Turbulence Data Using Wavelets and Time Series Decompositions.

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Long Description

This volume presents an overview of Bayesian methods for inference in the wavelet domain. The papers in this volume are divided into six parts: The first two papers introduce basic concepts. Chapters in Part II explore different approaches to prior modeling, using independent priors. Papers in the Part III discuss decision theoretic aspects of such prior models. In Part IV, some aspects of prior modeling using priors that account for dependence are explored. Part V considers the use of 2-dimensional wavelet decomposition in spatial modeling. Chapters in Part VI discuss the use of empirical Bayes estimation in wavelet based models. Part VII concludes the volume with a discussion of case studies using wavelet based Bayesian approaches. The cooperation of all contributors in the timely preparation of their manuscripts is greatly recognized. We decided early on that it was impor

Description for Sales People

This book links two of the most active areas of statistics, Bayesian statistics and wavelets.

Details ISBN0387988858 Author Peter Müller Short Title BAYESIAN INFERENCE IN WAVELET- Series Lecture Notes in Statistics Language English ISBN-10 0387988858 ISBN-13 9780387988856 Media Book Format Paperback DEWEY 519.542 Series Number 141 Year 1999 Imprint Springer-Verlag New York Inc. Place of Publication New York, NY Country of Publication United States Edition 1st Edited by Peter Müller Pages 396 DOI AU Release Date 1999-06-22 NZ Release Date 1999-06-22 US Release Date 1999-06-22 UK Release Date 1999-06-22 Publisher Springer-Verlag New York Inc. Edition Description Softcover reprint of the original 1st ed. 1999 Publication Date 1999-06-22 Illustrations XIV, 396 p. Audience Postgraduate, Research & Scholarly Country of Origin US Product Class Description Mathematics Alternative 9781461205678
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