Bayesian generalized product partition model
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 Pólya 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. 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.
Duke Scholars
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Related Subject Headings
- Statistics & Probability
- 4905 Statistics
- 0801 Artificial Intelligence and Image Processing
- 0199 Other Mathematical Sciences
- 0104 Statistics
Citation
Published In
ISSN
Publication Date
Volume
Issue
Start / End Page
Related Subject Headings
- Statistics & Probability
- 4905 Statistics
- 0801 Artificial Intelligence and Image Processing
- 0199 Other Mathematical Sciences
- 0104 Statistics