Description: Bayesian Hierarchical Models by Peter D. Congdon This is the second edition of a book on applied Bayesian modelling using WinBUGS. It has been updated with a new chapter on regression for causal effects, and one on computing options and strategies. FORMAT Hardcover LANGUAGE English CONDITION Brand New Publisher Description 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 books website Author Biography Peter Congdon is Research Professor in Quantitative Geography and Health Statistics at Queen Mary, University of London. Table of Contents ContentsPreface1. Bayesian Methods for Complex Data: Estimation and Inference2. Bayesian Analysis Options in R, and Coding for BUGS, JAGS, and Stan3. Model Fit, Comparison, and Checking4. Borrowing Strength via Hierarchical Estimation5. Time Structured Priors6. Representing Spatial Dependence7. Regression Techniques Using Hierarchical Priors8. Bayesian Multilevel Models9. Factor Analysis, Structural Equation Models, and Multivariate Priors10. Hierarchical Models for Longitudinal Data11. Survival and Event History Models12. Hierarchical Methods for Nonlinear and Quantile Regression Review "...The material covered in the almost 600 pages is broad, rich, and presented in a dense and conciseway. There is a notable emphasis on longitudinal models, spatial applications as well as structural equations models, which seems natural given the focus on hierarchicalmodels...The readership that will benefit most from the book might be statisticians with intermediateor advanced-level expertise in Bayesian statistics and at least some basic experience in the software implementation of Bayesian modeling techniques. The second edition is particularly worthwhile since it catches up with the computational developments of the last decade. Overall, the book nicely illustrates the richness and the flexibility of hierarchical modeling options within the Bayesian framework."- Christian Stock, Biometrical Journal, October 2020 Review Quote "...The material covered in the almost 600 pages is broad, rich, and presented in a dense and conciseway. There is a notable emphasis on longitudinal models, spatial applications as well as structural equations models, which seems natural given the focus on hierarchicalmodels...The readership that will benefit most from the book might be statisticians with intermediateor advanced-level expertise in Bayesian statistics and at least some basic experience in the software implementation of Bayesian modeling techniques. The second edition is particularly worthwhile since it catches up with the computational developments of the last decade. Overall, the book nicely illustrates the richness and the flexibility of hierarchical modeling options within the Bayesian framework." - Christian Stock, Biometrical Journal, October 2020 Details ISBN1498785751 Author Peter D. Congdon Publisher Taylor & Francis Inc Year 2019 Edition 2nd ISBN-10 1498785751 ISBN-13 9781498785754 Format Hardcover Subtitle With Applications Using R, Second Edition Place of Publication Portland Country of Publication United States Replaces 9781584887201 Affiliation Queen Mary University of London, UK Pages 580 DEWEY 519.542 Short Title Bayesian Hierarchical Models Language English Publication Date 2019-09-30 AU Release Date 2019-09-30 NZ Release Date 2019-09-30 US Release Date 2019-09-30 UK Release Date 2019-09-30 Illustrations 25 Tables, black and white; 70 Illustrations, black and white Edition Description 2nd edition Alternative 9781032177151 Audience Professional & Vocational Imprint Chapman & Hall/CRC We've got this At The Nile, if you're looking for it, we've got it. With fast shipping, low prices, friendly service and well over a million items - you're bound to find what you want, at a price you'll love! TheNile_Item_ID:125976043;
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ISBN-13: 9781498785754
Book Title: Bayesian Hierarchical Models
Item Height: 254 mm
Item Width: 178 mm
Author: Peter D. Congdon
Publication Name: Bayesian Hierarchical Models: with Applications Using R, Second Edition
Format: Hardcover
Language: English
Publisher: Taylor & Francis Inc
Subject: Biology, Mathematics
Publication Year: 2019
Type: Textbook
Item Weight: 1211 g
Number of Pages: 580 Pages
