Bayesian Inference in Wavelet-Based Models

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. Autor: Peter Müller, Brani Vidakovic Wydawnictwo: Springer Rok wydania: 2005 Okładka: miękka Liczba stron: 416 Wymiary: 23.5 x 15.5 x 2.5 cm Język: angielski ISBN: 9780387988856