Book description
This book provides an accessible approach to Bayesian computing and data analysis, with an emphasis on the interpretation of real data sets. Following in the tradition of the successful first edition, this book aims to make a wide range of statistical modeling applications accessible using tested code that can be readily adapted to the reader's own applications. The second edition has been thoroughly reworked and updated to take account of advances in the field. A new set of worked examples is included. The novel aspect of the first edition was the coverage of statistical modeling using WinBUGS and OPENBUGS. This feature continues in the new edition along with examples using R to broaden appeal and for completeness of coverage.
Table of contents
 Cover
 Title Page
 WILEY SERIES IN PROBABILITY AND STATISTICS
 Copyright
 Preface
 Chapter 1: Bayesian methods and Bayesian estimation

Chapter 2: Hierarchical models for related units
 2.1 Introduction: Smoothing to the hyper population
 2.2 Approaches to model assessment: Penalised fit criteria, marginal likelihood and predictive methods
 2.3 Ensemble estimates: Poisson–gamma and Betabinomial hierarchical models
 2.4 Hierarchical smoothing methods for continuous data
 2.5 Discrete mixtures and dirichlet processes
 2.6 General additive and histogram smoothing priors
 Exercises
 Notes
 References
 Chapter 3: Regression techniques

Chapter 4: More advanced regression techniques
 4.1 Introduction
 4.2 Departures from linear model assumptions and robust alternatives
 4.3 Regression for overdispersed discrete outcomes
 4.4 Link selection
 4.5 Discrete mixture regressions for regression and outlier status
 4.6 Modelling nonlinear regression effects
 4.7 Quantile regression
 Exercises
 Notes
 References
 Chapter 5: Metaanalysis and multilevel models
 Chapter 6: Models for time series

Chapter 7: Analysis of panel data
 7.1 Introduction
 7.2 Hierarchical longitudinal models for metric data
 7.3 Normal linear panel models and normal linear growth curves
 7.4 Longitudinal discrete data: Binary, categorical and Poisson panel data
 7.5 Random effects selection
 7.6 Missing data in longitudinal studies
 Exercises
 Notes
 References

Chapter 8: Models for spatial outcomes and geographical association
 8.1 Introduction
 8.2 Spatial regressions and simultaneous dependence
 8.3 Conditional prior models
 8.4 Spatial covariation and interpolation in continuous space
 8.5 Spatial heterogeneity and spatially varying coefficient priors
 8.6 Spatiotemporal models
 8.7 Clustering in relation to known centres
 Exercises
 Notes
 References

Chapter 9: Latent variable and structural equation models
 9.1 Introduction
 9.2 Normal linear structural equation models
 9.3 Dynamic factor models, panel data factor models and spatial factor models
 9.4 Latent trait and latent class analysis for discrete outcomes
 9.5 Latent trait models for multilevel data
 9.6 Structural equation models for missing data
 Exercises
 Notes
 References
 Chapter 10: Survival and event history models
 Index
 WILEY SERIES IN PROBABILITY AND STATISTICS
 End User License Agreement
Product information
 Title: Applied Bayesian Modelling, 2nd Edition
 Author(s):
 Release date: July 2014
 Publisher(s): Wiley
 ISBN: 9781119951513
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