Event Date
Speaker: Michele Guindani (Statistics, UC Irvine)
Title: “A Common Atom Model for the Bayesian Nonparametric Analysis of Nested Data”
Abstract: The use of large datasets for targeted therapeutic interventions requires new ways to characterize the heterogeneity observed across subgroups of a specific population. In particular, models for partially exchangeable data are needed for inference on nested datasets, where the observations are assumed to be organized in different units and some sharing of information is required to learn distinctive features of the units. In this talk, we propose a nested Common Atoms Model (CAM) that is particularly suited for the analysis of nested datasets where the distributions of the units are expected to differ only over a small fraction of the observations sampled from each unit. The proposed CAM allows a two-layered clustering at the distributional and observational level and is amenable to scalable posterior inference through the use of a computationally efficient nested slice sampler algorithm. We further discuss how to extend the proposed modeling framework to handle discrete measurements, and we conduct posterior inference on a real microbiome dataset from a diet swap study to investigate how the alterations in intestinal microbiota composition are associated with different eating habits. If time allows, we will also discuss an application to the analysis of time series calcium imaging experiments in awake behaving animals. We further investigate the performance of our model in capturing true distributional structures in the population by means of simulation studies.
About the speaker: Michele Guindani is a professor in the Department of Statistics at the University of California, Irvine.
He is a fellow of the American Statistical Association, and his research focuses on Bayesian analysis and Bayesian non-parametrics.
Seminar Date/Time: Thursday May 20, 4:10pm
This seminar will be delivered remotely via Zoom. To access the Zoom meeting for this seminar, please contact the instructor Professor Jairo Fùquene Patiño or Pete Scully (pscully@ucdavis.edu) for the meeting ID and password, stating your affiliation.