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Bayesian Essentials with R

2013-10-28 Computers
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Release : 2013-10-28
Genre : Computers
Kind : eBook
Book Rating : 874/5 ( reviews)

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Book Synopsis Bayesian Essentials with R by : Jean-Michel Marin

Download or read book Bayesian Essentials with R written by Jean-Michel Marin. This book was released on 2013-10-28. Available in PDF, EPUB and Kindle. Book excerpt: This Bayesian modeling book provides a self-contained entry to computational Bayesian statistics. Focusing on the most standard statistical models and backed up by real datasets and an all-inclusive R (CRAN) package called bayess, the book provides an operational methodology for conducting Bayesian inference, rather than focusing on its theoretical and philosophical justifications. Readers are empowered to participate in the real-life data analysis situations depicted here from the beginning. Special attention is paid to the derivation of prior distributions in each case and specific reference solutions are given for each of the models. Similarly, computational details are worked out to lead the reader towards an effective programming of the methods given in the book. In particular, all R codes are discussed with enough detail to make them readily understandable and expandable. Bayesian Essentials with R can be used as a textbook at both undergraduate and graduate levels. It is particularly useful with students in professional degree programs and scientists to analyze data the Bayesian way. The text will also enhance introductory courses on Bayesian statistics. Prerequisites for the book are an undergraduate background in probability and statistics, if not in Bayesian statistics.

Bayesian Core: A Practical Approach to Computational Bayesian Statistics

2007-05-26 Mathematics
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Release : 2007-05-26
Genre : Mathematics
Kind : eBook
Book Rating : 830/5 ( reviews)

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Book Synopsis Bayesian Core: A Practical Approach to Computational Bayesian Statistics by : Jean-Michel Marin

Download or read book Bayesian Core: A Practical Approach to Computational Bayesian Statistics written by Jean-Michel Marin. This book was released on 2007-05-26. Available in PDF, EPUB and Kindle. Book excerpt: This Bayesian modeling book is intended for practitioners and applied statisticians looking for a self-contained entry to computational Bayesian statistics. Focusing on standard statistical models and backed up by discussed real datasets available from the book website, it provides an operational methodology for conducting Bayesian inference, rather than focusing on its theoretical justifications. Special attention is paid to the derivation of prior distributions in each case and specific reference solutions are given for each of the models. Similarly, computational details are worked out to lead the reader towards an effective programming of the methods given in the book.

Bayesian Essentials with R

2013-11-30
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Release : 2013-11-30
Genre :
Kind : eBook
Book Rating : 886/5 ( reviews)

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Book Synopsis Bayesian Essentials with R by : Jean-Michel Marin

Download or read book Bayesian Essentials with R written by Jean-Michel Marin. This book was released on 2013-11-30. Available in PDF, EPUB and Kindle. Book excerpt:

Bayesian Computation with R

2009-04-20 Mathematics
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Release : 2009-04-20
Genre : Mathematics
Kind : eBook
Book Rating : 989/5 ( reviews)

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Book Synopsis Bayesian Computation with R by : Jim Albert

Download or read book Bayesian Computation with R written by Jim Albert. This book was released on 2009-04-20. Available in PDF, EPUB and Kindle. Book excerpt: 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).

Bayesian Data Analysis, Third Edition

2013-11-01 Mathematics
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Release : 2013-11-01
Genre : Mathematics
Kind : eBook
Book Rating : 954/5 ( reviews)

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Book Synopsis Bayesian Data Analysis, Third Edition by : Andrew Gelman

Download or read book Bayesian Data Analysis, Third Edition written by Andrew Gelman. This book was released on 2013-11-01. Available in PDF, EPUB and Kindle. Book excerpt: Now in its third edition, this classic book is widely considered the leading text on Bayesian methods, lauded for its accessible, practical approach to analyzing data and solving research problems. Bayesian Data Analysis, Third Edition continues to take an applied approach to analysis using up-to-date Bayesian methods. The authors—all leaders in the statistics community—introduce basic concepts from a data-analytic perspective before presenting advanced methods. Throughout the text, numerous worked examples drawn from real applications and research emphasize the use of Bayesian inference in practice. New to the Third Edition Four new chapters on nonparametric modeling Coverage of weakly informative priors and boundary-avoiding priors Updated discussion of cross-validation and predictive information criteria Improved convergence monitoring and effective sample size calculations for iterative simulation Presentations of Hamiltonian Monte Carlo, variational Bayes, and expectation propagation New and revised software code The book can be used in three different ways. For undergraduate students, it introduces Bayesian inference starting from first principles. For graduate students, the text presents effective current approaches to Bayesian modeling and computation in statistics and related fields. For researchers, it provides an assortment of Bayesian methods in applied statistics. Additional materials, including data sets used in the examples, solutions to selected exercises, and software instructions, are available on the book’s web page.

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