Stat Cafe - Dr. Bani Mallick
Bayesian Local Models Using Partitions
- Time: Tuesday 9/9/2025 from 11:10 AM to 12:25 PM
- Location: BLOC 448
Description
Not all models are wrong—in fact, some may even be correct, at least locally. Moreover, models can be highly useful, particularly when they provide insights or predictions that would otherwise be difficult to obtain. Building on this principle, I propose the use of Bayesian local models based on partitioning. Specifically, the Bayesian partition model constructs complex models by dividing the covariate space into disjoint regions, with each region represented by a simpler model. The main challenge lies in adaptively determining these local regions (partitions), a task that can be addressed using Voronoi tessellations, graphs or tree-based methods. We will discuss different modeling strategies and their applications in this framework.
Our Speaker
Dr. Bani K. Mallick is a Distinguished Professor, Regents Professor and Susan M. Arseven`75 Chair in Data Science and Computational Statistics in the Department of Statistics at Texas A&M University in College Station. He is the Director of the NSF TRIPODS Institute of Data science and the Center for Statistical Bioinformatics. Dr. Mallick is well known for his contribution to the theory and practice of Bayesian Semiparametric methods and Uncertainty Quantification. He is an elected fellow of American Association for the Advancement of Science, American Statistical Association, Institute of Mathematical Statistics, International Statistical Institute and the Royal Statistical Society. He received the Distinguished research awards, Distinguished graduate student Mentoring award from Texas A&M University and the Young Researcher award from the International Indian Statistical Association. Mallick’s areas of research include semiparametric classification and regression, hierarchical spatial modeling, inverse problem, uncertainty quantification and Bioinformatics. He has coauthored or co-edited six books and more than 200 research articles.
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