Bayesian hierarchical models: with applications using R

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"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 …

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Title Statements

Full title :Bayesian hierarchical models : with applications using R, / by Peter D. Congdon.

Summary

Edition Statements

Publication Date:2020
Edition:Second Edition

Publication Statements

Publisher(s):CRC Press, Taylor & Francis Group
Place of Publication:Boca Raton, FL
Language:eng
Publication Date:2020

Notes

Revised edition of: Applied Bayesian hierarchical methods. c2010.

The Physical Object

Description:1 online resource (xi, 579 pages) : : illustrations

Edition Identifiers

ISBN:9780429113352, 0429113358, 9781498785914, 1498785913, 9780429532900, 0429532903, 9780429547607, 0429547609
DOI:10.1201/9780429113352
oclc:on1120692089
LC Classification:QA279.5
Dewey Decimal:519.5/42

Bayesian methods for complex data: estimation and inference -- Computing options and strategies -- Model fit, comparison, and checking -- Borrowing strength estimation for exchangeable units -- Structured priors recognizing similarity over time and space -- Regression techniques using hierarchical priors -- Multilevel models -- Regression for causal effects with observational data -- Hierarchical models for panel data -- Multivariate priors, with a focus on factor and structural equation models -- Survival and event history models -- Hierarchical methods for nonlinear regression

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