Ronald Christensen, Wesley Johnson, Adam Branscum, Timothy E Hanson

July 2, 2010
by CRC Press

Textbook
- 516 Pages
- 87 B/W Illustrations

ISBN 9781439803547 - CAT# K10199

Series: Chapman & Hall/CRC Texts in Statistical Science

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- Covers a large number of statistical models
- Emphasizes the elicitation of reasonable prior information
- Explores numerical approximations via simulation
- Uses WinBUGS and R for computational problems
- Reviews basic concepts of matrix algebra and probability
- Includes numerous exercises and real-world examples throughout
- Provides data, programming code, and other materials at www.stat.unm.edu/~fletcher

Emphasizing the use of WinBUGS and R to analyze real data, **Bayesian Ideas and Data Analysis****: An Introduction for Scientists and Statisticians** presents statistical tools to address scientific questions. It highlights foundational issues in statistics, the importance of making accurate predictions, and the need for scientists and statisticians to collaborate in analyzing data. The WinBUGS code provided offers a convenient platform to model and analyze a wide range of data.

The first five chapters of the book contain core material that spans basic Bayesian ideas, calculations, and inference, including modeling one and two sample data from traditional sampling models. The text then covers Monte Carlo methods, such as Markov chain Monte Carlo (MCMC) simulation. After discussing linear structures in regression, it presents binomial regression, normal regression, analysis of variance, and Poisson regression, before extending these methods to handle correlated data. The authors also examine survival analysis and binary diagnostic testing. A complementary chapter on diagnostic testing for continuous outcomes is available on the book’s website. The last chapter on nonparametric inference explores density estimation and flexible regression modeling of mean functions.

The appropriate statistical analysis of data involves a collaborative effort between scientists and statisticians. Exemplifying this approach, **Bayesian Ideas and Data Analysis** focuses on the necessary tools and concepts for modeling and analyzing scientific data.

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