Centered Partition Processes: Informative Priors for Clustering (with Discussion).

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 provid

SUBMITTER: Paganin S 

PROVIDER: S-EPMC9364237 | biostudies-literature | 2021 Mar

REPOSITORIES: biostudies-literature