Bayesian inference in wavelet-based models

the long version

"This volume provides a thorough introduction and reference for any researcher who is interested in Bayesian inference for wavelet-based models. 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."--BOOK JACKET.