{"product_id":"9780387988856","title":"Bayesian Inference in Wavelet-Based Models","description":"\u003cp\u003eThis 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.\u003c\/p\u003e","brand":"Springer New York","offers":[{"title":"Default Title","offer_id":47016436891888,"sku":"9780387988856","price":127.99,"currency_code":"USD","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0737\/7593\/9824\/files\/9780387988856_p0.jpg?v=1763696796","url":"https:\/\/shop-qa.barnesandnoble.com\/products\/9780387988856","provider":"Barnes \u0026 Noble (DEV)","version":"1.0","type":"link"}