| 书目名称 | Prior Processes and Their Applications | | 副标题 | Nonparametric Bayesi | | 编辑 | Eswar G. Phadia | | 视频video | | | 概述 | Presents a systematic and comprehensive treatment of various prior processes.Provides valuable resource for nonparametric Bayesian analysis of big data.Includes a section on machine learning.Shows pra | | 丛书名称 | Springer Series in Statistics | | 图书封面 |  | | 描述 | .This book presents a systematic and comprehensive treatment of various prior processes that have been developed over the past four decades for dealing with Bayesian approach to solving selected nonparametric inference problems. This revised edition has been substantially expanded to reflect the current interest in this area. After an overview of different prior processes, it examines the now pre-eminent Dirichlet process and its variants including hierarchical processes, then addresses new processes such as dependent Dirichlet, local Dirichlet, time-varying and spatial processes, all of which exploit the countable mixture representation of the Dirichlet process. It subsequently discusses various neutral to right type processes, including gamma and extended gamma, beta and beta-Stacy processes, and then describes the Chinese Restaurant, Indian Buffet and infinite gamma-Poisson processes, which prove to be very useful in areas such as machine learning, information retrieval and featural modeling. Tailfree and Polya tree and their extensions form a separate chapter, while the last two chapters present the Bayesian solutions to certain estimation problems pertaining to the distributio | | 出版日期 | Book 2016Latest edition | | 关键词 | Bayesian nonparametrics; Dirichlet process; survival function; high dimensional data; machine learning | | 版次 | 2 | | doi | https://doi.org/10.1007/978-3-319-32789-1 | | isbn_softcover | 978-3-319-81370-7 | | isbn_ebook | 978-3-319-32789-1Series ISSN 0172-7397 Series E-ISSN 2197-568X | | issn_series | 0172-7397 | | copyright | Springer International Publishing AG, part of Springer Nature 2016 |
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发表于 2025-3-21 18:23:56
