Bayesian Statistical Methods provides data scientists with the foundational and computational tools needed to carry out a Bayesian analysis. This book focuses on Bayesian methods applied routinely in practice including multiple linear regression, mixed effects models and generalized linear models (GLM). The authors include many examples with complete R code and comparisons with analogous frequentist procedures.

In addition to the basic concepts of Bayesian inferential methods, the book covers many general topics:

Advice on selecting prior distributions

Computational methods including Markov chain Monte Carlo (MCMC)

Model-comparison and goodness-of-fit measures, including sensitivity to priors
Frequentist properties of Bayesian methods

Case studies covering advanced topics illustrate the flexibility of the Bayesian approach:

Semiparametric regression

Handling of missing data using predictive distributions

Priors for high-dimensional regression models

Computational techniques for large datasets

Spatial data analysis

The advanced topics are presented with sufficient conceptual depth that the reader will be able to carry out such analysis and argue the relative merits of Bayesian and classical methods. A repository of R code, motivating data sets, and complete data analyses are available on the book’s website.

About the Author

Brian J. Reich, Associate Professor of Statistics at North Carolina State University, is currently the editor-in-chief of the Journal of Agricultural, Biological, and Environmental Statistics and was awarded the LeRoy & Elva Martin Teaching Award.

Sujit K. Ghosh, Professor of Statistics at North Carolina State University, has over 22 years of research and teaching experience in conducting Bayesian analyses, received the Cavell Brownie mentoring award, and served as the Deputy Director at the Statistical and Applied Mathematical Sciences Institute

"About this title" may belong to another edition of this title.

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