Prior Processes and Their Applications: Nonparametric Bayesian Estimation

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Item specifics

Condition
Brand New: A new, unread, unused book in perfect condition with no missing or damaged pages. See all condition definitionsopens in a new window or tab
MPN
1 black & white illustrations, biography
Brand
Springer
Style
ABIS_BOOK
ISBN
9783642392795
Category

About this product

Product Identifiers

Publisher
Springer Berlin / Heidelberg
ISBN-10
3642392792
ISBN-13
9783642392795
eBay Product ID (ePID)
166531929

Product Key Features

Number of Pages
Xiv, 207 Pages
Language
English
Publication Name
Prior Processes and Their Applications : Nonparametric Bayesian Estimation
Subject
Probability & Statistics / General, Probability & Statistics / Bayesian Analysis
Publication Year
2013
Type
Textbook
Subject Area
Mathematics
Author
Eswar G. Phadia
Format
Hardcover

Dimensions

Item Height
0.2 in
Item Weight
162.8 Oz
Item Length
9.3 in
Item Width
6.1 in

Additional Product Features

Intended Audience
Scholarly & Professional
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)
Dewey Edition
23
Number of Volumes
1 vol.
Illustrated
Yes
Dewey Decimal
519.54
Synopsis
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.
LC Classification Number
QA276-280

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