ArticleBayesian analysis2021
Centered Partition Processes: Informative Priors for Clustering (with Discussion).
Article in Bayesian analysis, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- BAYESIAN LEARNING OF CLINICALLY MEANINGFUL SEPSIS PHENOTYPES IN NORTHERN TANZANIA.The annals of applied statistics · 2025Article
- Bayesian Clustering via Fusing of Localized Densities.Journal of the American Statistical Association · 2025Article
- Clustering computer mouse tracking data with informed hierarchical shrinkage partition priors.Biometrics · 2024Article
- Spectral Clustering, Bayesian Spanning Forest, and Forest Process.Journal of the American Statistical Association · 2024Article
- Bayesian cluster analysis.Philosophical transactions. Series A, Mathematical, physical, and engineering sciences · 2023Review
- Dimension-Grouped Mixed Membership Models for Multivariate Categorical Data.Journal of machine learning research : JMLR · 2023Article
- MULTIVARIATE MIXED MEMBERSHIP MODELING: INFERRING DOMAIN-SPECIFIC RISK PROFILES.The annals of applied statistics · 2022Article
- Centered Partition Processes: Informative Priors for Clustering (with Discussion).Bayesian analysis · 2021Article
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Authors and funding
5 authors.
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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 in terms of Exchangeable Partition Probability Functions (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. For example, we are motivated by an epidemiological application, in which we wish to cluster birth defects into groups and we have prior knowledge of an initial clustering provided by experts. As a general approach for including such prior knowledge, we propose a Centered Partition (CP) process that modifies the EPPF to favor partitions close to an initial one. Some properties of the CP prior are described, a general algorithm for posterior computation is developed, and we illustrate the methodology through simulation examples and an application to the motivating epidemiology study of birth defects.
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