Bayesian Models for Categorical Data
Peter Congdon
copyright 2005 John Wiley & Sons, Ltd
chapter1 Principles of Baysian Inference
chapter2 Model Comparison and choice
chapter3 Regression for Metric Outcomes
chapter4 Models for Binary and Count Outcomes
chapter5 Further Questions in Binomial and Count Regression
chapter6 Random Effect and Latent Variable Models for Multicategory outcomes
chapter7 Ordinal regression
chapter8 Discrete Spatial Data
chapter9 Time Series Models for Discrete Variables
chapter10 Hierarchical and Panel Data Models
chapter11 Missing Data Models
chapter12 Index
The use of Bayesian methods for the analysis of data has grown substantially in areas as diverse as applied statistics, psychology, economics and medical science. Bayesian Methods for Categorical Data sets out to demystify modern Bayesian methods, makingthem accessible to students and researchers alike. Emphasizing the use of statistical computing and applied data analysis, this book provides a comprehensive introduction to Bayesian methods of categorical outcomes. * Reviews recent Bayesian methodologyThe use of Bayesian methods for the analysis of data has grown substantially in areas as diverse as applied statistics, psychology, economics and medical science. Bayesian Methods for Categorical Data sets out to demystify modern Bayesian methods, makingthem accessible to students and researchers alike. Emphasizing the use of statistical computing and applied data analysis, this book provides a comprehensive introduction to Bayesian methods of categorical outcomes. * Reviews recent Bayesian methodology
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