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 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