An Introduction to Applied Bayesian Modeling
- Available for pre-order. Item will ship after January 25, 2022
An engaging, sophisticated, and fun introduction to the field of Bayesian Statistics, Bayes Rules! An Introduction to Bayesian Modeling with R brings the power of modern Bayesian thinking, modeling, and computing to a broad audience. In particular, it is an ideal resource for advanced undergraduate Statistics students and practitioners with comparable experience.
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.
Table of Contents
List of Tables
List of Figures
About the Author
Chapter 1 The Big (Bayesian) Picture
Chapter 2 Bayes’ Rule
Chapter 3 The Beta-Binomial Bayesian Model
Chapter 4 Balance and Sequentiality in Bayesian Analyses
Chapter 5 Conjugate Families Chapter 6 Approximating the Posterior
Chapter 7 MCMC Under the Hood
Chapter 8 Posterior Inference and Prediction
Chapter 9 Simple Normal Regression
Chapter 10 Evaluating Regression Models
Chapter 11 Extending the Normal Regression Model
Chapter 12 Poisson and Negative Binomial Regression
Chapter 13 Logistic Regression
Chapter 14 Naive Bayes Classification
Chapter 15 Hierarchical Models are Exciting
Chapter 16 (Normal) Hierarchical Models Without Predictors
Chapter 17 (Normal) Hierarchical Models With Predictors
Chapter 18 Non-Normal Hierarchical Regression & Classification
Chapter 19 Adding More Layers Bibliography Index
Alicia Johnson is an Associate Professor of Statistics at Macalester College in Saint Paul, Minnesota. She enjoys exploring and connecting students to Bayesian analysis, computational statistics, and the power of data in contributing to this shared world of ours.
Miles Ott is a Senior Data Scientist at The Janssen Pharmaceutical Companies of Johnson & Johnson. Prior to his current position, he taught at Carleton College, Augsburg University, and Smith College. He is interested in biostatistics, LGBTQ+ health research, analysis of social network data, and statistics/data science education. He blogs at milesott.com and tweets about statistics, gardening, and his dogs on Twitter.
Mine Dogucu is an Assistant Professor of Teaching in the Department of Statistics at University of California Irvine. She spends majority of her time thinking about what to teach, how to teach it, and what tools to use while teaching. She likes intersectional feminism, cats, and R Ladies. She tweets about statistics and data science education on Twitter.