Abstract: The Bayesian approach is well known for using priors to improve inference, but equally important part is the integration over the uncertainties. I first present recent development in hierarchical shrinkage priors for presenting sparsity assumptions in covariate effects. I then present a projection predictive variable selection approach, which is a Bayesian decision theoretical approach for variable selection which can preserve the essential information and uncertainties related to all variables in the study. I also present recent excellent experimental results and easy to use software.

Speaker: Aki Vehtari

Affiliation: Professor of Computer Science, Aalto University

Place of Seminar: University of Helsinki