Скачать или смотреть François Caron (University of Oxford) - Confidence with a Robust Bayesian Touch

Abstract: Bayes-assisted frequentist approaches enable the construction of fixed-n confidence intervals or time-uniform confidence sequences with frequentist coverage guarantees, while incorporating prior information the user may have about a parameter of interest. The key advantage of such methods is that, when the data align with the prior, they yield shorter intervals; when they do not, coverage remains valid. In this talk, I will discuss several properties of these procedures, particularly when employing “robust” priors. I will also present applications of these methods to prediction-powered inference—a framework that provides valid statistical inference when an experimental dataset is supplemented by predictions from a black-box machine learning model. Joint work with Stefano Cortinovis and Valentin Kilian