September, 2007
Starting with a carefully formulated Dirichlet process (DP) mixture model, we derive a generalized product partition model (GPPM) in which the partition process is predictor-dependent. The GPPM generalizes DP clustering to relax the exchangeability assumption through the incorporation of predictors, resulting in a generalized P\'olya urn scheme. In addition, the GPPM can be used for formulating flexible semiparametric Bayes models for conditional distribution estimation, bypassing the need for expensive computation of large numbers of unknowns characterizing priors for dependent collections of random probability measures. Properties are discussed, a variety of special cases are considered, and an efficient Gibbs sampling algorithm is developed for posterior computation. The methods are illustrated using simulation examples and an epidemiologic application.
Keywords: Clustering; Conditional distribution estimation; Dirichlet process; Generalized P\'olya urn; Latent class model; Mixture of experts; Nonparametric Bayes; Product partition.
The manuscript is available in PDF formats.