Bayesian Statistical Methods Av Brian J. (n.c. State University) Reich, Sujit K. Ghosh

<P><STRONG>Bayesian Statistical Methods</STRONG> 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.</P><P>In addition to the basic concepts of Bayesian inferential methods, the book covers many general topics: </P><UL><P><LI>Advice on selecting prior distributions</LI><P></P><P><LI>Computational methods including Markov chain Monte Carlo (MCMC) </LI><P></P><P><LI>Model-comparison and goodness-of-fit measures, including sensitivity to priors</LI><P></P><P><LI>Frequentist properties of Bayesian methods</LI><P></P></UL><P>Case studies covering advanced topics illustrate the flexibility of the Bayesian approach:</P><UL><P><LI>Semiparametric regression </LI><P><

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