Prior Processes and Their Applications

Nonparametric Bayesian Estimation

Authors: Phadia, Eswar G.

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About this book

This book presents a systematic and comprehensive treatment of various prior processes that have been developed over the last four decades in order to deal with the Bayesian approach to solving some nonparametric inference problems. Applications of these priors in various estimation problems are presented. Starting with the famous Dirichlet process and its variants, the first part describes processes neutral to the right, gamma and extended gamma, beta and beta-Stacy, tail free and Polya tree, one and two parameter Poisson-Dirichlet, the Chinese Restaurant and Indian Buffet processes, etc., and discusses their interconnection. In addition, several new processes that have appeared in the literature in recent years and which are off-shoots of the Dirichlet process are described briefly. The second part contains the Bayesian solutions to certain estimation problems pertaining to the distribution function and its functional based on complete data. Because of the conjugacy property of some of these processes, the resulting solutions are mostly in closed form. The third part treats similar problems but based on right censored data. Other applications are also included. A comprehensive list of references is provided in order to help readers explore further on their own.

About the authors

Eswar Phadia received his doctorate from Ohio State University and has been on the faculty of William Paterson University of New Jersey for nearly four decades, during which he has served as Chairman of the Department, Director of Research and Dean of the College of Science and Health. He has published numerous papers in the areas of Nonparametric Bayesian Inference, Survival Analysis, and Decision Theory in scientific journals including the Annals of Statistics. He has been the recipient of several NSF grants, State grants and University awards. He was a visiting faculty/scholar at UCLA, Harvard, UC, Davis, and spent sabbaticals at Rutgers, Columbia and the University of Pennsylvania. He has presented papers at professional meetings nationally and internationally and has given seminars and lectures in the United States and in Canada, China, India, Jordan and Singapore. He is a member of the Institute of Mathematical Statistics, the American Statistical Association and an elected member of the International Statistical Institute.

Reviews

From the book reviews:

“The book under review is likely to be of use to graduate students and researchers interested in prior processes and their applications to Bayesian nonparametrics.” (Ross S. McVinish, Mathematical Reviews, June, 2014)


Table of contents (3 chapters)

Buy this book

eBook $59.99
price for USA (gross)
  • ISBN 978-3-642-39280-1
  • Digitally watermarked, DRM-free
  • Included format: PDF, EPUB
  • ebooks can be used on all reading devices
  • Immediate eBook download after purchase
Hardcover $79.99
price for USA
  • ISBN 978-3-642-39279-5
  • Free shipping for individuals worldwide
  • Usually dispatched within 3 to 5 business days.
Rent the ebook  
  • Rental duration: 1 or 6 month
  • low-cost access
  • online reader with highlighting and note-making option
  • can be used across all devices
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Bibliographic Information

Bibliographic Information
Book Title
Prior Processes and Their Applications
Book Subtitle
Nonparametric Bayesian Estimation
Authors
Copyright
2013
Publisher
Springer-Verlag Berlin Heidelberg
Copyright Holder
Springer-Verlag Berlin Heidelberg
eBook ISBN
978-3-642-39280-1
DOI
10.1007/978-3-642-39280-1
Hardcover ISBN
978-3-642-39279-5
Edition Number
1
Number of Pages
XIV, 207
Topics