From
Basi6 International, Irving, TX, U.S.A.
Seller rating 5 out of 5 stars
AbeBooks Seller since June 24, 2016
New. Delivery takes 25-30 days. Excellent Customer Service. Seller Inventory # POD-558468
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.
Features:
About the Author:
Peter Congdon is Research Professor in Quantitative Geography and Health Statistics at Queen Mary, University of London.
Title: Bayesian Hierarchical Models
Publisher: Chapman and Hall/CRC
Publication Date: 2019
Binding: Hardcover
Condition: Brand New
Edition: 2nd Edition