ABSTRACT

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.

part Unit I|124 pages

Bayesian Foundations

chapter 21|14 pages

The Big (Bayesian) Picture

chapter 2|32 pages

Bayes' Rule

chapter 3|26 pages

The Beta-Binomial Bayesian Model

chapter 5|28 pages

Conjugate Families

part Unit II|84 pages

Posterior Simulation & Analysis

chapter 1266|32 pages

Approximating the Posterior

chapter 7|24 pages

MCMC under the Hood

chapter 8|26 pages

Posterior Inference & Prediction

part Unit III|164 pages

Bayesian Regression & Classification

chapter 2109|32 pages

Simple Normal Regression

chapter 10|24 pages

Evaluating Regression Models

chapter 11|36 pages

Extending the Normal Regression Model

chapter 12|26 pages

Poisson & Negative Binomial Regression

chapter 13|26 pages

Logistic Regression

chapter 14|18 pages

Naive Bayes Classification

part Unit IV|138 pages

Hierarchical Bayesian models

chapter 37415|12 pages

Hierarchical Models are Exciting

chapter 17|42 pages

(Normal) Hierarchical Models with Predictors

chapter 19|26 pages

Adding More Layers