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.