Applied Bayesian Modelling

Peter Congdon

• Publisher: Wiley
• Year: 2014
• Print ISBN: 9781119951513
• eBook ISBN: 9781118895061
• Edition: 2
• Topic: Mathematics
• Subtopic: Probability & Statistics
• Index: Mathematics

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
• Chapter 3: Regression techniques
• Chapter 4: More advanced regression techniques
• Chapter 5: Meta-analysis and multilevel models
• Chapter 6: Models for time series
• Chapter 7: Analysis of panel data
• Chapter 8: Models for spatial outcomes and geographical association
• Chapter 9: Latent variable and structural equation models
• Chapter 10: Survival and event history models
• Index
• WILEY SERIES IN PROBABILITY AND STATISTICS
• End User License Agreement