Bayesian Hierarchical Models | With Applications Using R, Second Edition

Peter D. Congdon

ISBN 10: 1032177152 / ISBN 13: 9781032177151

Published by Chapman and Hall/CRC, 2021

About this Item

Bayesian Hierarchical Models | With Applications Using R, Second Edition | Peter D. Congdon | Taschenbuch | Einband - flex.(Paperback) | Englisch | 2021 | Chapman and Hall/CRC | EAN 9781032177151 | Verantwortliche Person für die EU: Libri GmbH, Europaallee 1, 36244 Bad Hersfeld, gpsr[at]libri[dot]de | Anbieter: preigu.

Seller Inventory # 121356799

Bibliographic Details

• Title: Bayesian Hierarchical Models | With Applications Using R, Second Edition
• Publisher: Chapman and Hall/CRC
• Publication Date: 2021
• Language: English
• ISBN 10: 1032177152
• ISBN 13: 9781032177151
• Binding: Taschenbuch
• Edition: 2nd Edition
• Condition: Neu
• Dimensions: 254W x 178H x 32D millimeters 254 millimeters width by 178 millimeters height by 32 millimeters depth
• Weight: 1101 grams
• Seller catalogs:
  • Bücher

About this title

Synopsis

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

About the Author

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