Bayesian Statistical Methods

Bayesian Statistical Methods provides data scientists with the foundational and computational tools needed to carry out a Bayesian analysis, with examples and comparisons with frequentist procedures. It covers general topics such as selecting prior distributions, computational methods, model-comparison, and case studies.

Format: Hardback
Publication date: 11 April 2019
Publisher: Taylor & Francis Inc

Bayesian Statistical Methods is a comprehensive guide for data scientists seeking to employ Bayesian analysis in their work. This book delves into various Bayesian methods, including multiple linear regression, mixed effects models, and generalized linear models (GLM), which are widely used in practice. The authors provide numerous examples accompanied by complete R code, allowing readers to understand and apply the techniques effectively.

In addition to covering the core concepts of Bayesian inferential methods, the book explores a wide range of general topics. It offers guidance on selecting appropriate prior distributions, computational methods such as Markov chain Monte Carlo (MCMC), model comparison and goodness-of-fit measures, including sensitivity to priors, and the frequentist properties of Bayesian methods. Case studies covering advanced topics demonstrate the flexibility and power of the Bayesian approach, including semiparametric regression, handling of missing data using predictive distributions, priors for high-dimensional regression models, computational techniques for large datasets, and spatial data analysis.

The advanced topics are presented with sufficient conceptual depth to enable readers to carry out such analysis and critically evaluate the relative merits of Bayesian and classical methods. The book also includes a repository of R code, motivating data sets, and complete data analyses available on its website.

Brian J. Reich, Associate Professor of Statistics at North Carolina State University, serves as the editor-in-chief of the Journal of Agricultural, Biological, and Environmental Statistics, and has been recognized with the LeRoy & Elva Martin Teaching Award. Sujit K. Ghosh, Professor of Statistics at North Carolina State University, brings over 22 years of research and teaching experience in conducting Bayesian analysis.

Overall, Bayesian Statistical Methods is an essential resource for data scientists and researchers interested in leveraging Bayesian inference for statistical modeling and inference. Its comprehensive coverage, practical examples, and extensive R code repository make it an invaluable tool for advancing statistical knowledge and practice.

Weight: 578g
Dimension: 164 x 242 x 17 (mm)
ISBN-13: 9780815378648