Title: Constructing a Disease Outbreak Forecast Ensemble via Bayesian Gibbs Posterior Stacking

Presented by Spencer Wadsworth, Postdoctoral Scholar, University of Connecticut

DATE:  Wednesday, October 8, 2025, 3:30 PM, AUST 434

Webex link: Link 

Coffee will be available at 3:00 in the Noether Lounge (AUST 326)

Abstract: In collaborative forecast projects, the combining of multiple probabilistic forecasts into an ensemble is standard practice, with linear pooling being a common combination method. The weighting scheme of a linear pool should be tailored to the specific research question, and weight selection is often performed via optimizing a proper scoring rule. This is known as optimal linear pooling. Besides optimal linear pooling, Bayesian predictive synthesis has emerged as a model probability updating scheme which is more flexible than standard Bayesian model averaging and which provides a Bayesian solution to selecting model weights for a linear pool. In many problems, equally weighted linear pool forecasts often outperform forecasts constructed using sophisticated weight selection methods. Thus, regularization to an equal weighting of forecasts may be a valuable addition to any weight selection method. In this presentation, I introduce an optimal linear pool based on a Gibbs posterior over stacked model weights optimized over a proper scoring rule. The Gibbs posterior extends stacking into a Bayesian framework by allowing for optimal weight solutions to be influenced by a prior distribution, and it also provides uncertainty quantification of weights in the form of a probability distribution. I compare ensemble forecast performance with model averaging methods and equal weighted models in simulation studies and in a real data example from the 2023-24 US Centers for Disease Control FluSight competition. In both the simulation studies and the FluSightanalysis, the stacked Gibbs posterior produces ensemble forecasts which often outperform the ensembles of other methods.

Bio: I earned my PhD in statistics at Iowa State University in 2024 and am currently a postdoc in the UConn department of statistics working with Ofer Harel. My research interests include Bayesian hierarchical and nonparametric modeling with applications to disease outbreak forecasting and brain imaging data.  I am obsessed with BYU sports and can hardly wait for them to play UConn in men's basketball next month!