Bayesian Inference in Wavelet-Based Models

Brani Vidakovic

ISBN 10: 0387988858 / ISBN 13: 9780387988856

Published by Springer New York, Springer US Jun 1999, 1999

About this Item

This item is printed on demand - Print on Demand Titel. Neuware -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 tant to referee and critically evaluate the papers which were submitted for inclusion in this volume. For this substantial task, we relied on the service of numerous referees to whom we are most indebted. We are also grateful to John Kimmel and the Springer-Verlag referees for considering our proposal in a very timely manner. Our special thanks go to our spouses, Gautami and Draga, for their support.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 416 pp. Englisch.

Seller Inventory # 9780387988856

Bibliographic Details

• Title: Bayesian Inference in Wavelet-Based Models
• Publisher: Springer New York, Springer US Jun 1999
• Publication Date: 1999
• Language: English
• ISBN 10: 0387988858
• ISBN 13: 9780387988856
• Binding: Taschenbuch
• Condition: Neu

About this title

Synopsis

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 tant to referee and critically evaluate the papers which were submitted for inclusion in this volume. For this substantial task, we relied on the service of numerous referees to whom we are most indebted. We are also grateful to John Kimmel and the Springer-Verlag referees for considering our proposal in a very timely manner. Our special thanks go to our spouses, Gautami and Draga, for their support.

About the Author

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