Bayesian Computation with R (Use R!) and over 2 million other books are available for Amazon Kindle . Learn more
£38.93
  • RRP: £42.99
  • You Save: £4.06 (9%)
FREE Delivery in the UK.
In stock.
Dispatched from and sold by Amazon.
Gift-wrap available.
Quantity:1
Trade in your item
Get a £8.77
Gift Card.
Have one to sell?
Flip to back Flip to front
Listen Playing... Paused   You're listening to a sample of the Audible audio edition.
Learn more
See this image

Bayesian Computation with R (Use R!) Paperback – 15 May 2009


See all 4 formats and editions Hide other formats and editions
Amazon Price New from Used from
Kindle Edition
"Please retry"
Paperback
"Please retry"
£38.93
£28.72 £26.20

Frequently Bought Together

Bayesian Computation with R (Use R!) + Bayesian Data Analysis, Third Edition (Chapman & Hall/CRC Texts in Statistical Science) + Doing Bayesian Data Analysis: A Tutorial with R and BUGS
Price For All Three: £131.16

Buy the selected items together


Trade In this Item for up to £8.77
Trade in Bayesian Computation with R (Use R!) for an Amazon Gift Card of up to £8.77, which you can then spend on millions of items across the site. Trade-in values may vary (terms apply). Learn more

Product details

  • Paperback: 312 pages
  • Publisher: Springer; 2 edition (15 May 2009)
  • Language: English
  • ISBN-10: 0387922970
  • ISBN-13: 978-0387922973
  • Product Dimensions: 15.2 x 1.8 x 22.9 cm
  • Average Customer Review: 4.0 out of 5 stars  See all reviews (5 customer reviews)
  • Amazon Bestsellers Rank: 683,627 in Books (See Top 100 in Books)
  • See Complete Table of Contents

More About the Author

Discover books, learn about writers, and more.

Product Description

Review

new text

From the Back Cover

There has been a dramatic growth in the development and application of Bayesian inferential methods. Some of this growth is due to the availability of powerful simulation-based algorithms to summarize posterior distributions. There has been also a growing interest in the use of the system R for statistical analyses. R's open source nature, free availability, and large number of contributor packages have made R the software of choice for many statisticians in education and industry.

Bayesian Computation with R introduces Bayesian modeling by the use of computation using the R language. The early chapters present the basic tenets of Bayesian thinking by use of familiar one and two-parameter inferential problems. Bayesian computational methods such as Laplace's method, rejection sampling, and the SIR algorithm are illustrated in the context of a random effects model. The construction and implementation of Markov Chain Monte Carlo (MCMC) methods is introduced. These simulation-based algorithms are implemented for a variety of Bayesian applications such as normal and binary response regression, hierarchical modeling, order-restricted inference, and robust modeling. Algorithms written in R are used to develop Bayesian tests and assess Bayesian models by use of the posterior predictive distribution. The use of R to interface with WinBUGS, a popular MCMC computing language, is described with several illustrative examples.

This book is a suitable companion book for an introductory course on Bayesian methods and is valuable to the statistical practitioner who wishes to learn more about the R language and Bayesian methodology. The LearnBayes package, written by the author and available from the CRAN website, contains all of the R functions described in the book.

The second edition contains several new topics such as the use of mixtures of conjugate priors and the use of Zellner’s g priors to choose between models in linear regression. There are more illustrations of the construction of informative prior distributions, such as the use of conditional means priors and multivariate normal priors in binary regressions. The new edition contains changes in the R code illustrations according to the latest edition of the LearnBayes package.

Jim Albert is Professor of Statistics at Bowling Green State University. He is Fellow of the American Statistical Association and is past editor of The American Statistician. His books include Ordinal Data Modeling (with Val Johnson), Workshop Statistics: Discovery with Data, A Bayesian Approach (with Allan Rossman), and Bayesian Computation using Minitab.


Inside This Book (Learn More)
Browse Sample Pages
Front Cover | Copyright | Table of Contents | Excerpt | Index
Search inside this book:

Customer Reviews

4.0 out of 5 stars
Share your thoughts with other customers

Most Helpful Customer Reviews

15 of 15 people found the following review helpful By PietaGina on 15 Dec 2008
Format: Paperback
If you are familiar with R and with Bayesian Computation this book may be a good introduction to using R packages for Bayesian Computation, but I didn't feel it is a good introduction to Bayesian Computation or to R if you are unfamiliar with either or both.
Comment Was this review helpful to you? Yes No Sending feedback...
Thank you for your feedback. If this review is inappropriate, please let us know.
Sorry, we failed to record your vote. Please try again
Format: Kindle Edition Verified Purchase
This extensive (over 200 pages) book is intended to assist the use of the R programming language for Bayesian statistical calculations.
It takes a step-by-step approach, using straightforward practical examples immediately.
It introduces built-in R functions appropriate to each specific example and reviews them briefly at the end of each one.
It starts with some simple standard descriptive statistical examples and includes useful graphical R plotting functions to display helpful diagrams. It assumes you have intermediate statistical knowledge and can move beyond the simple descriptive stats work with relative ease. It then uses these concepts to introduce Bayesian statistics after a very short introduction. In effect, you should already be aware of the principles of the Bayesian approach (initial belief modelled via prior distributions, these modified through measurements expressed in posterior distributions which are then used to draw inferences). I had some of this knowledge, but swiftly realised I would need to devote serious study to grasp it thoroughly. This is an excellent book, with an extensive bibliography, to help you along the way.
Comment Was this review helpful to you? Yes No Sending feedback...
Thank you for your feedback. If this review is inappropriate, please let us know.
Sorry, we failed to record your vote. Please try again
7 of 12 people found the following review helpful By W. Shareef on 1 July 2010
Format: Paperback
First two chapters are easy to follow, after that its down-hill. A lot of equations are stated without an explanation, no clear expositions. Writer falls into the trap of writing a book for himself and not the student. Nothing is explained clearly. No one in the 'real' world uses R, only mathematics departments in Universities, so you want be applying these functions in work related environments. No answers given to chapter questions. The book is called 'Bayesian computation in R', only if you understand Bayesian computation very well and need to see some examples in R. Otherwise your not going to learn Bayesian stats with this book.
2 Comments Was this review helpful to you? Yes No Sending feedback...
Thank you for your feedback. If this review is inappropriate, please let us know.
Sorry, we failed to record your vote. Please try again
2 of 6 people found the following review helpful By A. Hansen on 23 Mar 2010
Format: Paperback
I love this book. It is rather condensed and technical and you will likely need a more general intro to Bayesian stats to accompany it, but that being said it's a really neat book.
Comment Was this review helpful to you? Yes No Sending feedback...
Thank you for your feedback. If this review is inappropriate, please let us know.
Sorry, we failed to record your vote. Please try again
0 of 3 people found the following review helpful By Blaise F. Egan on 22 Sep 2010
Format: Paperback Verified Purchase
This little book gets you started with R (a powerful open source statistical toolkit) and shows you how to do a variety of sophisticated Bayesian statistical analyses.
Comment Was this review helpful to you? Yes No Sending feedback...
Thank you for your feedback. If this review is inappropriate, please let us know.
Sorry, we failed to record your vote. Please try again


Feedback