Doing Bayesian Data Analysis: A Tutorial with R, Jags, and Stan by John KruschkeMy rating: 5 of 5 stars
Both textbook and practical guide, this work is an accessible approach to Bayesian data analysis from the basics. Chapter-length explorations of various implementations make this an effective reference for non-expert practitioners that seek to bring the value of Bayesian analysis to problems in their field. Intended for first-year graduate students or advanced undergraduates, this book offers thorough training on modern Bayesian methods for data analysis. Algebra and basic calculus, nothing really beyond simple integration, are prerequisites to maximizing understanding of the theory presented in the primer, but no more mathematics than that. This book features throughout implementations in the programming language R and software packages JAGS and Stan. A comfort level with basic computer programming will bring out the greatest value here. Certainly, someone looking to create a first-time R application featuring Bayesian analysis will be hard pressed to find a better text resource to contribute toward success. As a textbook, the basics of probability and random sampling are effectively covered. Each chapter has a few, generally less than ten, exercises. Many are new or revised and they are thought-provoking, multi-step applications. They have explicit purposes (“Transformed parameters in Stan, and comparison with JAGS”) and guidelines for accomplishment. In a classroom setting, I think further contouring of exercises would be required to level-set expectations.
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