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Bayesian Hierarchical Models

Peter D. Congdon
€137.99
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applied modeling
Author_Peter D. Congdon
Bayesian hierarchical models
BUGS Code
Category=PBT
Category=PS
causal effects
causal inference methods
Conditional Autoregressive Priors
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Full Conditional Densities
Full Conditionals
hierarchical Bayesian modeling in R
HMC
Inverse Wishart Priors
Jags
Linear Mixed Model
longitudinal modeling
MCMC Algorithm
MCMC Diagnostics
MCMC Estimation
MCMC Method
MCMC Sample
MCMC Sampling
MCMC Step
multilevel analysis
Multiple Linear Regression
Non-parametric Regression
Normal Linear Mixed Model
Posterior Density
practical data analysis
Precision Matrix
Proposal Density
Quantile Regression
R
R-based Bayesian computing
Random Effect Standard Deviations
random effects
Random Intercept Variation
regression structures
Residual Spatial Dependence
spatial statistics
Stan
statistical computing
survival analysis techniques
WinBUGS

Product details

  • ISBN 9781498785754
  • Weight: 1740g
  • Dimensions: 178 x 254mm
  • Publication Date: 30 Sep 2019
  • Publisher: Taylor & Francis Inc
  • Publication City/Country: US
  • Product Form: Hardback
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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 book’s website
About Peter D. Congdon

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

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