2026 Colloquia

Title to be announced

Eric Laber, PhD
Duke University

Thursday, April 16, 2026
3:30 p.m. - 5:00 p.m.
Helen Wood Hall, Room 1W-501

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Monte Carlo Inference for Semiparametric Bayesian Regression

Daniel Kowal, PhD
Cornell University

Data transformations are essential for broad applicability of parametric regression models. However, for Bayesian analysis, joint inference of the transformation and model parameters typically involves restrictive parametric transformations or nonparametric representations that are computationally inefficient and cumbersome for implementation and theoretical analysis, which limits their usability in practice. We introduce a simple, general, and efficient strategy for joint posterior inference of an unknown transformation and all regression model parameters. The proposed approach delivers (1) joint posterior consistency under general conditions, including multiple model misspecifications, and (2) efficient Monte Carlo (not MCMC) sampling for the transformation and all parameters for important special cases. We illustrate the methodology for simulated and real data analysis with semiparametric Bayesian linear models, quantile regression, and Gaussian processes. These tools apply across a variety of data domains, including real-valued, positive, compactly-supported, and discrete (e.g., count) data.
Thursday, March 12, 2026