A fully Bayesian sparse polynomial chaos expansion approach with joint priors on the coefficients and global selection of terms

Paul-Christian Bürkner, Ilja Kröker, Sergey Oladyshkin, Wolfgang Nowak

Abstract: Highlights•Polynomial Chaos expansions are surrogates for computationally expensive models.•A key problem is the number of polynomial expansion terms in high dimensions.•We regularize the polynomial coefficients with a Bayesian joint shrinkage prior.•This is combined with statistical methods for Bayesian variable selection.•Our method outperforms existing methods in terms of accuracy and predictive performance.