1st Edition

Bayes Rules!
An Introduction to Applied Bayesian Modeling




  • Available for pre-order. Item will ship after January 25, 2022
ISBN 9780367255398
January 25, 2022 Forthcoming by Chapman and Hall/CRC
552 Pages 134 Color & 102 B/W Illustrations

USD $79.95

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Book Description

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

Preface

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

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Author(s)

Biography

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.