Peter Congdon 
Bayesian Models for Categorical Data 

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,
making them 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 for categorical outcomes
(binary, count and multinomial data).

* Considers missing data models techniques and non-standard models
(ZIP and negative binomial).

* Evaluates time series and spatio-temporal models for discrete
data.

* Features discussion of univariate and multivariate
techniques.

* Provides a set of downloadable worked examples with documented
Win BUGS code, available from an ftp site.

The author’s previous 2 bestselling titles provided a comprehensive
introduction to the theory and application of Bayesian models.
Bayesian Models for Categorical Data continues to build upon this
foundation by developing their application to categorical, or
discrete data – one of the most common types of data available. The
author’s clear and logical approach makes the book accessible to a
wide range of students and practitioners, including those dealing
with categorical data in medicine, sociology, psychology and
epidemiology.

关于作者

Peter Congdon, Queen Mary, University of London, UK

Peter is the author of two best-selling Wiley books on Bayesian
modelling – Bayesian Statistical Modelling, and
Applied Bayesian Modelling.