Biostatistics Dept Seminar: Common atom mixture models in two biostatistical inference problems
Biostatistics Department Seminar
Title: Common atom mixture models in two biostatistical inference problems
Abstract: We consider two examples of statistical inference for two related populations. In one example we characterize two patient populations that are relevant in the construction of a clinical study design, and propose a method to adjust for detected differences. The second example is about comparative immune profiling under two biologic conditions of interest when we identify shared versus condition-specific homogeneous cell subpopulations. Bayesian inference in both applications requires prior probability models for two or more related distributions. We build on extensive literature on such models based on Dirichlet process priors. Models are commonly known as dependent Dirichlet processes (DDP), with many variations and extensions beyond the Dirichlet process model. The special feature in the two motivating applications is the focus on differences in the heterogeneity of the related populations, with one application aiming to adjust for such differences, and the other application aiming to identify and understand immune cell subtypes that are characteristic for one or the other condition.
We briefly review the extensive literature on DDP models and then introduce variations of DDP priors suitable for these inference goals. In both applications the underlying model structures are common atoms mixture models with highly structured priors on the weights.
Speakers
Peter Mueller is a Professor of Statistics and Mathematics at The University of Texas at Austin. He works on Bayesian inference, with a focus on nonparametric Bayesian methods, simulation based methods, optimal design and multiple comparison procedures.
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2024-2025 Monday Seminar Series
All seminars are held at 12:05 PM via Zoom and onsite. View all seminar information here.