Bayes Rules!: An Introduction to Applied Bayesian Modeling

by Alicia A Johnson, Miles Q. Ott, Mine Dogucu

An engaging, sophisticated, and fun introduction to the field of Bayesian statistics, Bayes Rules!: An Introduction to Applied Bayesian Modeling brings the power of modern Bayesian thinking, modeling, and computing to a broad audience. In particular, the book is an ideal resource for advanced undergraduate statistics students and practitioners with comparable experience. the book assumes that readers are familiar with the content covered in a typical undergraduate-level introductory statistics course. Readers will also, ideally, have some experience with undergraduate-level probability, calculus, and the R statistical software. Readers without this background will still be able to follow along so long as they
are eager to pick up these tools on the fly as all R code is provided.Bayes Rules! empowers readers to weave Bayesian approaches into their everyday practice. Discussions and applications are data driven. A natural progression from fundamental to multivariable, hierarchical models emphasizes a practical and generalizable model building process. The evaluation of these Bayesian models reflects the fact that a data analysis does not exist in a vacuum. Features • Utilizes data-driven examples and exercises. • Emphasizes the iterative model building and evaluation process. • Surveys an interconnected range of multivariable regression and classification models. • Presents fundamental Markov chain Monte Carlo simulation. • Integrates R code, including RStan modeling tools and the bayesrules package. • Encourages readers to tap into their intuition and learn by doing. • Provides a friendly and inclusive introduction to technical Bayesian concepts. • Supports Bayesian applications with foundational Bayesian theory.

Genres: Mathematics, Nonfiction

544 pages, Hardcover

Published March 4, 2022

Reviews

[Xi'an · Rating 4 out of 5]

As far as I can tell [from abroad and from only teaching students with a math background], Bayes Rules! seems to be catering to early (US) undergraduate students with very little exposure to mathematical statistics or probability, as it introduces basic probability notions like pmf, joint distribution, and Bayes’ theorem (as well as Greek letters!) and shies away from integration or algebra. As an alternative to “technical derivations” Bayes Rules! centres on intuition and simulation (yay!) via its bayesrule R package. Itself relying on rstan. Learning from example (as R code is always provided), the book proceeds through conjugate priors, MCMC (Metropolis-Hasting) methods, regression models, and hierarchical regression models. Quite impressive given the limited prerequisites set by the authors. (I appreciated the representations of the prior-likelihood-posterior, especially in the sequential case.) The examples and exercises are diverse (if mostly US centric), modern (including cultural references that completely escape me), and often reflect on the authors’ societal concerns. In particular, their concern about a fair use of the inferred models is preminent, even though a quantitative assessment of the degree of fairness would require a much more advanced perspective than the book allows.

[Logan Burton · Rating 4 out of 5]

Once again I review obscure Econometrics textbooks on my Goodreads. I think you'll be hard-pressed to find a book as clear and comprehensive on Bayesian stats for an advanced undergraduate/intro graduate student. Bonus points on having the entire book be free on the internet with all the R code.

The only thing I would lobby against this book is how confusing and unintuitive its chapter on the Monte Carlo Markov Chains are. I must have spent a good three weeks wrapping my head around what they were trying to say.

[Kate O'Hanlon · Rating 5 out of 5]

Without a doubt the most clear and accessible text book that I encountered during my MSc Data Analytics. Sticks to practical approaches with lots of worked examples and will not veer off into dense mathematical notation. You are unlikely to become an expert reading this book, but nor are you likely to stare at in in uncomprehending horror.
One for those of us who just want to get through it.