Monte Carlo Methods In Bayesian Computation

Author : Ming-Hui Chen,Qi-Man Shao,Joseph G. Ibrahim
Publisher : Springer Science & Business Media
Page : 399 pages
File Size : 51,8 Mb
Release : 2012-12-06
Category : Mathematics
ISBN : 9781461212768

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Monte Carlo Methods in Bayesian Computation by Ming-Hui Chen,Qi-Man Shao,Joseph G. Ibrahim Pdf

Dealing with methods for sampling from posterior distributions and how to compute posterior quantities of interest using Markov chain Monte Carlo (MCMC) samples, this book addresses such topics as improving simulation accuracy, marginal posterior density estimation, estimation of normalizing constants, constrained parameter problems, highest posterior density interval calculations, computation of posterior modes, and posterior computations for proportional hazards models and Dirichlet process models. The authors also discuss model comparisons, including both nested and non-nested models, marginal likelihood methods, ratios of normalizing constants, Bayes factors, the Savage-Dickey density ratio, Stochastic Search Variable Selection, Bayesian Model Averaging, the reverse jump algorithm, and model adequacy using predictive and latent residual approaches. The book presents an equal mixture of theory and applications involving real data, and is intended as a graduate textbook or a reference book for a one-semester course at the advanced masters or Ph.D. level. It will also serve as a useful reference for applied or theoretical researchers as well as practitioners.

Author : Ming-Hui Chen,Qi-Man Shao,Joseph George Ibrahim
Publisher : Unknown
Page : 386 pages
File Size : 50,9 Mb
Release : 2002
Category : Electronic
ISBN : OCLC:641915469

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Monte Carlo Methods in Bayesian Computation by Ming-Hui Chen,Qi-Man Shao,Joseph George Ibrahim Pdf

Author : Dani Gamerman,Hedibert F. Lopes
Publisher : CRC Press
Page : 352 pages
File Size : 55,6 Mb
Release : 2006-05-10
Category : Mathematics
ISBN : 1584885874

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Markov Chain Monte Carlo by Dani Gamerman,Hedibert F. Lopes Pdf

While there have been few theoretical contributions on the Markov Chain Monte Carlo (MCMC) methods in the past decade, current understanding and application of MCMC to the solution of inference problems has increased by leaps and bounds. Incorporating changes in theory and highlighting new applications, Markov Chain Monte Carlo: Stochastic Simulation for Bayesian Inference, Second Edition presents a concise, accessible, and comprehensive introduction to the methods of this valuable simulation technique. The second edition includes access to an internet site that provides the code, written in R and WinBUGS, used in many of the previously existing and new examples and exercises. More importantly, the self-explanatory nature of the codes will enable modification of the inputs to the codes and variation on many directions will be available for further exploration. Major changes from the previous edition: · More examples with discussion of computational details in chapters on Gibbs sampling and Metropolis-Hastings algorithms · Recent developments in MCMC, including reversible jump, slice sampling, bridge sampling, path sampling, multiple-try, and delayed rejection · Discussion of computation using both R and WinBUGS · Additional exercises and selected solutions within the text, with all data sets and software available for download from the Web · Sections on spatial models and model adequacy The self-contained text units make MCMC accessible to scientists in other disciplines as well as statisticians. The book will appeal to everyone working with MCMC techniques, especially research and graduate statisticians and biostatisticians, and scientists handling data and formulating models. The book has been substantially reinforced as a first reading of material on MCMC and, consequently, as a textbook for modern Bayesian computation and Bayesian inference courses.

Author : Faming Liang,Chuanhai Liu,Raymond Carroll
Publisher : John Wiley & Sons
Page : 308 pages
File Size : 52,8 Mb
Release : 2011-07-05
Category : Mathematics
ISBN : 9781119956808

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Advanced Markov Chain Monte Carlo Methods by Faming Liang,Chuanhai Liu,Raymond Carroll Pdf

Markov Chain Monte Carlo (MCMC) methods are now an indispensable tool in scientific computing. This book discusses recent developments of MCMC methods with an emphasis on those making use of past sample information during simulations. The application examples are drawn from diverse fields such as bioinformatics, machine learning, social science, combinatorial optimization, and computational physics. Key Features: Expanded coverage of the stochastic approximation Monte Carlo and dynamic weighting algorithms that are essentially immune to local trap problems. A detailed discussion of the Monte Carlo Metropolis-Hastings algorithm that can be used for sampling from distributions with intractable normalizing constants. Up-to-date accounts of recent developments of the Gibbs sampler. Comprehensive overviews of the population-based MCMC algorithms and the MCMC algorithms with adaptive proposals. This book can be used as a textbook or a reference book for a one-semester graduate course in statistics, computational biology, engineering, and computer sciences. Applied or theoretical researchers will also find this book beneficial.

Author : Dani Gamerman
Publisher : CRC Press
Page : 264 pages
File Size : 42,6 Mb
Release : 1997-10-01
Category : Mathematics
ISBN : 0412818205

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Markov Chain Monte Carlo by Dani Gamerman Pdf

Bridging the gap between research and application, Markov Chain Monte Carlo: Stochastic Simulation for Bayesian Inference provides a concise, and integrated account of Markov chain Monte Carlo (MCMC) for performing Bayesian inference. This volume, which was developed from a short course taught by the author at a meeting of Brazilian statisticians and probabilists, retains the didactic character of the original course text. The self-contained text units make MCMC accessible to scientists in other disciplines as well as statisticians. It describes each component of the theory in detail and outlines related software, which is of particular benefit to applied scientists.

Author : Jim Albert
Publisher : Springer Science & Business Media
Page : 304 pages
File Size : 42,8 Mb
Release : 2009-04-20
Category : Mathematics
ISBN : 9780387922980

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Bayesian Computation with R by Jim Albert Pdf

There has been dramatic growth in the development and application of Bayesian inference in statistics. Berger (2000) documents the increase in Bayesian activity by the number of published research articles, the number of books,andtheextensivenumberofapplicationsofBayesianarticlesinapplied disciplines such as science and engineering. One reason for the dramatic growth in Bayesian modeling is the availab- ity of computational algorithms to compute the range of integrals that are necessary in a Bayesian posterior analysis. Due to the speed of modern c- puters, it is now possible to use the Bayesian paradigm to ?t very complex models that cannot be ?t by alternative frequentist methods. To ?t Bayesian models, one needs a statistical computing environment. This environment should be such that one can: write short scripts to de?ne a Bayesian model use or write functions to summarize a posterior distribution use functions to simulate from the posterior distribution construct graphs to illustrate the posterior inference An environment that meets these requirements is the R system. R provides a wide range of functions for data manipulation, calculation, and graphical d- plays. Moreover, it includes a well-developed, simple programming language that users can extend by adding new functions. Many such extensions of the language in the form of packages are easily downloadable from the Comp- hensive R Archive Network (CRAN).

Author : Steve Brooks,Andrew Gelman,Galin Jones,Xiao-Li Meng
Publisher : CRC Press
Page : 620 pages
File Size : 55,8 Mb
Release : 2011-05-10
Category : Mathematics
ISBN : 9781420079425

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Handbook of Markov Chain Monte Carlo by Steve Brooks,Andrew Gelman,Galin Jones,Xiao-Li Meng Pdf

Since their popularization in the 1990s, Markov chain Monte Carlo (MCMC) methods have revolutionized statistical computing and have had an especially profound impact on the practice of Bayesian statistics. Furthermore, MCMC methods have enabled the development and use of intricate models in an astonishing array of disciplines as diverse as fisherie

Author : Arnaud Doucet,Nando de Freitas,Neil Gordon
Publisher : Springer Science & Business Media
Page : 590 pages
File Size : 50,7 Mb
Release : 2013-03-09
Category : Mathematics
ISBN : 9781475734379

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Sequential Monte Carlo Methods in Practice by Arnaud Doucet,Nando de Freitas,Neil Gordon Pdf

Monte Carlo methods are revolutionizing the on-line analysis of data in many fileds. They have made it possible to solve numerically many complex, non-standard problems that were previously intractable. This book presents the first comprehensive treatment of these techniques.

Author : Jun S. Liu
Publisher : Springer Science & Business Media
Page : 350 pages
File Size : 54,7 Mb
Release : 2013-11-11
Category : Mathematics
ISBN : 9780387763712

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Monte Carlo Strategies in Scientific Computing by Jun S. Liu Pdf

This book provides a self-contained and up-to-date treatment of the Monte Carlo method and develops a common framework under which various Monte Carlo techniques can be "standardized" and compared. Given the interdisciplinary nature of the topics and a moderate prerequisite for the reader, this book should be of interest to a broad audience of quantitative researchers such as computational biologists, computer scientists, econometricians, engineers, probabilists, and statisticians. It can also be used as a textbook for a graduate-level course on Monte Carlo methods.

Author : Christian Robert,George Casella
Publisher : Springer Science & Business Media
Page : 297 pages
File Size : 41,6 Mb
Release : 2010
Category : Computers
ISBN : 9781441915757

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Introducing Monte Carlo Methods with R by Christian Robert,George Casella Pdf

This book covers the main tools used in statistical simulation from a programmer’s point of view, explaining the R implementation of each simulation technique and providing the output for better understanding and comparison.

Author : James E. Gentle
Publisher : Springer Science & Business Media
Page : 252 pages
File Size : 44,5 Mb
Release : 2013-03-14
Category : Computers
ISBN : 9781475729603

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Random Number Generation and Monte Carlo Methods by James E. Gentle Pdf

Monte Carlo simulation has become one of the most important tools in all fields of science. This book surveys the basic techniques and principles of the subject, as well as general techniques useful in more complicated models and in novel settings. The emphasis throughout is on practical methods that work well in current computing environments.

Author : M. Antónia Amaral Turkman,Carlos Daniel Paulino,Peter Müller
Publisher : Cambridge University Press
Page : 256 pages
File Size : 49,8 Mb
Release : 2019-02-28
Category : Business & Economics
ISBN : 9781108481038

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Computational Bayesian Statistics by M. Antónia Amaral Turkman,Carlos Daniel Paulino,Peter Müller Pdf

This integrated introduction to fundamentals, computation, and software is your key to understanding and using advanced Bayesian methods.

Author : Osvaldo A. Martin,Ravin Kumar,Junpeng Lao
Publisher : CRC Press
Page : 420 pages
File Size : 48,7 Mb
Release : 2021-12-28
Category : Computers
ISBN : 9781000520040

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Bayesian Modeling and Computation in Python by Osvaldo A. Martin,Ravin Kumar,Junpeng Lao Pdf

Bayesian Modeling and Computation in Python aims to help beginner Bayesian practitioners to become intermediate modelers. It uses a hands on approach with PyMC3, Tensorflow Probability, ArviZ and other libraries focusing on the practice of applied statistics with references to the underlying mathematical theory. The book starts with a refresher of the Bayesian Inference concepts. The second chapter introduces modern methods for Exploratory Analysis of Bayesian Models. With an understanding of these two fundamentals the subsequent chapters talk through various models including linear regressions, splines, time series, Bayesian additive regression trees. The final chapters include Approximate Bayesian Computation, end to end case studies showing how to apply Bayesian modelling in different settings, and a chapter about the internals of probabilistic programming languages. Finally the last chapter serves as a reference for the rest of the book by getting closer into mathematical aspects or by extending the discussion of certain topics. This book is written by contributors of PyMC3, ArviZ, Bambi, and Tensorflow Probability among other libraries.

Author : W.R. Gilks,S. Richardson,David Spiegelhalter
Publisher : CRC Press
Page : 505 pages
File Size : 45,5 Mb
Release : 1995-12-01
Category : Mathematics
ISBN : 9781482214970

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Markov Chain Monte Carlo in Practice by W.R. Gilks,S. Richardson,David Spiegelhalter Pdf

In a family study of breast cancer, epidemiologists in Southern California increase the power for detecting a gene-environment interaction. In Gambia, a study helps a vaccination program reduce the incidence of Hepatitis B carriage. Archaeologists in Austria place a Bronze Age site in its true temporal location on the calendar scale. And in France,

Author : Ronald Cools,Dirk Nuyens
Publisher : Springer
Page : 624 pages
File Size : 43,5 Mb
Release : 2016-06-13
Category : Mathematics
ISBN : 9783319335070

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Monte Carlo and Quasi-Monte Carlo Methods by Ronald Cools,Dirk Nuyens Pdf

This book presents the refereed proceedings of the Eleventh International Conference on Monte Carlo and Quasi-Monte Carlo Methods in Scientific Computing that was held at the University of Leuven (Belgium) in April 2014. These biennial conferences are major events for Monte Carlo and quasi-Monte Carlo researchers. The proceedings include articles based on invited lectures as well as carefully selected contributed papers on all theoretical aspects and applications of Monte Carlo and quasi-Monte Carlo methods. Offering information on the latest developments in these very active areas, this book is an excellent reference resource for theoreticians and practitioners interested in solving high-dimensional computational problems, arising, in particular, in finance, statistics and computer graphics.