Download BookPrior Processes and Their Applications Nonparametric Bayesian Estimation (Springer Series in Statistics)

[Download PDF.GaQY] Prior Processes and Their Applications Nonparametric Bayesian Estimation (Springer Series in Statistics)

[Download PDF.GaQY] Prior Processes and Their Applications Nonparametric Bayesian Estimation (Springer Series in Statistics)

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Book Details :
Published on: 2016-07-28
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Original language: English

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 distribution function and its functional based on complete data as well as right censored data. Because of the conjugacy property of some of these processes, most solutions are presented in closed form. Lifetime Data Analysis - incl. option to publish open access Lifetime Data Analysis is the only journal dedicated to statistical methods and applications for lifetime data. The journal advances and promotes statistical science ... Monte Carlo method - Wikipedia Monte Carlo methods are very important in computational physics physical chemistry and related applied fields and have diverse applications from complicated ... Bayesian inference - Wikipedia Bayesian inference is a method of statistical inference in which Bayes' theorem is used to update the probability for a hypothesis as more evidence or information ... Econometrics Books This webpage provides recommendations for econometrics books. Options for undergraduate econometrics graduate econometrics and other fields are provided. The other ... Inferring From Data - home.ubalt.edu Tutorial describes time series analysis popular distributions and other topics. The Gaussian Processes Web Site The Gaussian Processes Web Site. This web site aims to provide an overview of resources concerned with probabilistic modeling inference and learning based on ... Taiji Suzuki's Homepage () - is.titech.ac.jp Department of Mathematical and Computing Sciences Graduate School of Information Science and Engineering Tokyo Institute of Technology Sakigake (PRESTO) JST ijpe-online.com - NEWS & CONFERENCES There is no single international journal at the moment that deals with the problem of performance of products systems and services in its totality as the ... Machine Learning Group Publications - University of Cambridge Gaussian Processes and Kernel Methods Gaussian processes are non-parametric distributions useful for doing Bayesian inference and learning on unknown functions. Dr. Arsham's Statistics Site - home.ubalt.edu The Birth of Probability and Statistics The original idea of"statistics" was the collection of information about and for the"state". The word statistics derives ...
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