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

Alicia A.Johnson is anAssociateProfessorofStatisticsatMacalesterCollegeinSaintPaul, Minnesota. SheenjoysexploringandconnectingstudentstoBayesiananalysis,computational statistics, andthepowerofdataincontributingtothissharedworldofours. Miles Q.Ott is aSeniorDataScientistatTheJanssenPharmaceuticalCompaniesof Johnson &Johnson.Priortohiscurrentposition,hetaughtatCarletonCollege,Augsburg University,andSmithCollege.Heisinterestedinbiostatistics,LGBTQ+healthresearch, analysis ofsocialnetworkdata,andstatistics/datascienceeducation.Heblogsatmilesott.com and tweetsonTwitter1 aboutstatistics,gardening,andhisdogs. Mine Dogucu is anAssistantProfessorofTeachingintheDepartmentofStatisticsat UniversityofCaliforniaIrvine.Shespendsthemajorityofhertimethinkingaboutwhatto teach,howtoteachit,andwhattoolstousewhileteaching.Shelikesintersectionalfeminism, cats, andRLadies.ShetweetsonTwitter2 aboutstatisticsanddatascienceeducation.

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.