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Activity Number: 33 - Junior Research in Methods for Integrating Heterogeneous Data: From Clustering to Factor Analysis
Type: Topic Contributed
Date/Time: Monday, August 3, 2020 : 10:00 AM to 11:50 AM
Sponsor: International Society for Bayesian Analysis (ISBA)
Abstract #312995
Title: Prior-Driven Cluster Allocation in Bayesian Mixture Models
Author(s): Sally Paganin* and Amy Herring and Adnrew Olshan and David Dunson
Companies: University of California, Berkeley and Duke University and UNC-Chapel Hill and Duke University
Keywords: Bayesian nonparametrics; Bayesian clustering; centered process; mixture model; product partition model
Abstract:

There is a very rich literature proposing Bayesian approaches for clustering starting with a prior probability distribution on partitions. Most approaches assume exchangeability, leading to simple representations of such prior in terms of an Exchangeable Partition Probability Function (EPPF). Gibbs-type priors encompass a broad class of such cases, including Dirichlet and Pitman-Yor processes. Even though there have been some proposals to relax the exchangeability assumption, allowing covariate-dependence and partial exchangeability, limited consideration has been given on how to include concrete prior knowledge on the partition. Our motivation is drawn from an epidemiological application, in which we wish to cluster birth defects into groups and we have a prior knowledge of an initial clustering provided by experts. The underlying assumption is that birth defects in the same group may have similar coefficients in logistic regression analysis relating different exposures to risk of developing the defect. As a general approach for including such prior knowledge, we propose a Centered Partition (CP) process that modifies a base EPPF to favor partitions in a convenient distance


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