Bayesian Hierarchical Models: With Applications Using R, Second Edition, 2nd Edition (Hardback) book cover

Bayesian Hierarchical Models

With Applications Using R, Second Edition, 2nd Edition

By Peter D. Congdon

Chapman and Hall/CRC

572 pages | 70 B/W Illus.

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Hardback: 9781498785754
pub: 2019-10-03
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An intermediate-level treatment of Bayesian hierarchical models and their applications, this book demonstrates the advantages of a Bayesian approach to data sets involving inferences for collections of related units or variables, and in methods where parameters can be treated as random collections. Through illustrative data analysis and attention to statistical computing, this book facilitates practical implementation of Bayesian hierarchical methods.

The new edition is a revision of the book Applied Bayesian Hierarchical Methods. It maintains a focus on applied modelling and data analysis, but now using entirely R based Bayesian computing options. It has been updated with a new chapter on regression for causal effects, and one on computing options and strategies. This latter chapter is particularly important, due to recent advances in Bayesian computing and estimation, including the development of rjags and rstan. It also features updates throughout with new examples.

The examples exploit and illustrate the broader advantages of the R computing environment, while allowing readers to explore alternative likelihood assumptions, regression structures, and assumptions on prior densities.


  • Provides a comprehensive and accessible overview of applied Bayesian hierarchical modelling
  • Includes many real data examples to illustrate different modelling topics
  • R code (based on rjags, jagsUI, R2OpenBUGS, and rstan) is integrated into the book, emphasizing implementation
  • Software options and coding principles are introduced in new chapter on computing
  • Programs and data sets available on the book’s website

Table of Contents

Chapter 1 Bayesian Methods for Complex Data: Estimation and Inference; Chapter 2 Computing Options and Strategies; Chapter 3 Model Fit, Comparison, and Checking; Chapter 4 Borrowing Strength Estimation for Exchangeable Units; Chapter 5 Structured Priors Recognizing Similarity over Time and Space; Chapter 6 Regression Techniques using Hierarchical Priors; Chapter 7 Multilevel Models; Chapter 8 Regression for Causal Effects with Observational Data; Chapter 9 Hierarchical Models for Panel Data; Chapter 10 Multivariate Priors, with a Focus on Factor and Structural Equation Models; Chapter 11 Survival and Event History Models; Chapter 12 Hierarchical Methods for Nonlinear Regression.

About the Author

Peter Congdon is Research Professor in Quantitative Geography and Health Statistics at Queen Mary, University of London.

Subject Categories

BISAC Subject Codes/Headings:
MATHEMATICS / Probability & Statistics / General