SMRI Seminar ’Finding structures in observations: consistent(?) clustering analysis’ Clara Grazian (University of Sydney) Tuesday, 10th May, 3:00pm - 4:00pm Quad S224 & Online via Zoom Register for online attendance here: https://uni-sydney.zoom.us/meeting/register/tZwtduCoqTMuGdXzQfyR4LE53QRP02ySGJdX Abstract: Clustering is an important task in almost every area of knowledge: medicine and epidemiology, genomics, environmental science, economics, visual sciences, among others. Methodologies to perform inference on the number of clusters have often been proved to be inconsistent and introducing a dependence structure among the clusters implies additional difficulties in the estimation process. In a Bayesian setting, clustering in the situation where the number of clusters is unknown is often performed by using Dirichlet process priors or finite mixture models. However, the posterior distributions on the number of groups have been recently proved to be inconsistent. This seminar aims at reviewing the Bayesian approaches available to perform via mixture models and give some new insights. Biography: Dr Clara Grazian received a joint PhD in 2016 from University Paris-Dauphine, France and Sapienza University of Rome, Italy, working on Bayesian analysis for mixture models and copula models. She then joined the Nuffield Department of Medicine and the Big Data Institute of the University of Oxford to work on an international project trying to investigate mechanisms of drug resistance developed by tuberculosis.