Bayesian Computation with R

by Jim Albert

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).

Genres: Mathematics, Nonfiction

312 pages, Kindle Edition

First published June 11, 2007

About the author

Jim Albert is a Distinguished University Professor of Statistics at Bowling Green State University. His research interests include Bayesian modeling and applications of statistical thinking in sports. He has authored or coauthored several books including Ordinal Data Modeling, Bayesian Computation with R, and Workshop Statistics: Discovery with Data, A Bayesian Approach.

Reviews

[Bing Wang · Rating 3 out of 5]

Heavily rely on Learn Bayes package. However, still a good introduction of playing bayes in r.

[Huong · Rating 5 out of 5]

Good step by step guidance. Code here http://personal.bgsu.edu/~albert/R.Sc...

[Hà Bùi · Rating 3 out of 5]

Failed to continue half the book. I would suggest Statistics Rethinking by Richard McElreath as it has better steps by steps guide

[Andy McKenzie · Rating 3 out of 5]

An excellent first five chapters which are pretty well documented and have nice code that works at his website. From there the quality is widely considered to drop off. I personally found the exercises a good way to learn basic things about the techniques and trade-offs involved in sampling from multivariate probability distributions.

[Sylvester · Rating 4 out of 5]

A well written guide to Bayesian R computation, one thing which really bugged me was the fact that the list of commands were given at the end of each chapter thus it can be confusing at first because we are not told what the commands are meant to do. A lot of the ideas could also be elaborated more too.