Bayesian Hierarchical Models

Language: English

Published by Taylor and Francis Ltd, GB, 2021

1032177152 / 9781032177151

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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.Features: 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.…

Seller Inventory # LU-9781032177151

Title
Bayesian Hierarchical Models
Author
Peter D. Congdon
Publisher
Taylor and Francis Ltd, GB
Publication year
2021
Condition
New
Binding
Paperback
Language
English
ISBN 10
1032177152
ISBN 13
9781032177151
Edition
2nd Edition
Item weight
1,093 grams

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