446 pages, July 2005

• 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 WinBUGS 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.

"…valuable for anyone interested in how Bayesian ideas apply in practice an should prove useful for anyone using the WINBUGS package for categorical data analysis." (Biometrics, March 2007)

"…an excellent resource for biostatisticians and medical researchers." (Doody's Health Services)

"…perfectly suited as a reference for any practitioner….Congdon has done a laudable job of introducing jointly the concepts of categorical data and Bayesian analysis." (Journal of the American Statistical Association, June 2006)