{"success":1,"msg":"","color":"rgb(28, 35, 49)","title":"Centered Partition Processes: Informative Priors for Clustering<\/b>","description":"webinar","title2":"","start":"2021-10-08 14:00","end":"2021-10-08 15:00","responsable":"Isabelle Beaudry <\/i><\/a>","speaker":"Sally Paganin (Harvard School of Public Health)","id":"52","type":"webinar","timezone":"America\/Santiago","activity":"https:\/\/zoom.us\/j\/95855178442?pwd=NjZFUkd1d1g3eit3RWF5TzJkTjBFZz09\r\nPasscode: 001416","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 application in birth defect epidemiology, 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 process that modifies the EPPF to favor partitions close to an initial one. We use our method to analyze data from the National Birth Defect Prevention Study (NBDPS), relating different exposures to the risk of developing a birth defect, focusing on the class of Congenital Heart Defects.\r\n\r\nThis is joint work with Amy Herring (Duke University), Andrew Olshan (UNC at Chapel Hill) and David Dunson (Duke University)"}