Monday 4 Mar, 4pm in room 903 Social Work Bldg:

We’ve Got More Than One Model: Evaluating, comparing, and extending Bayesian predictions

Methods in statistics and data science are often framed as solutions to particular problems, in which a particular model or method is applied to a dataset. But good practice typically requires multiplicity, in two dimensions: fitting many different models to better understand a single dataset, and applying a method to a series of different but related problems. To understand and make appropriate inferences from real-world data analysis, we should account for the set of models we might fit, and for the set of problems to which we would apply a method. This is known as the reference set in frequentist statistics or the prior distribution in Bayesian statistics. We shall discuss recent research of ours that addresses these issues, involving the following statistical ideas: Type M errors, the multiverse, weakly informative priors, Bayesian stacking and cross-validation, simulation-based model checking, divide-and-conquer algorithms, and validation of approximate computations. We will also discuss how this work is motivated by applications in political science, pharmacology, and other areas.