Bayesian generalized product partition model
Abstract
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.
Type
Journal articlePermalink
https://hdl.handle.net/10161/4623Collections
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Show full item recordScholars@Duke
David B. Dunson
Arts and Sciences Distinguished Professor of Statistical Science
My research focuses on developing new tools for probabilistic learning from complex
data - methods development is directly motivated by challenging applications in ecology/biodiversity,
neuroscience, environmental health, criminal justice/fairness, and more. We seek
to develop new modeling frameworks, algorithms and corresponding code that can be
used routinely by scientists and decision makers. We are also interested in new inference
framework and in studying theoretical properties

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