Bayesian adaptation of chaos representations using variational inference and sampling on geodesics.

ABSTRACT: A novel approach is presented for constructing polynomial chaos representations of scalar quantities of interest (QoI) that extends previously developed methods for adaptation in Homogeneous Chaos spaces. In this work, we develop a Bayesian formulation of the problem that characterizes the posterior distributions of the series coefficients and the adaptation rotation matrix acting on the Gaussian input variables. The adaptation matrix is thus construed as a new parameter of the map from input to QoI, estimated through Bayesian inference. For the computation of the coefficients' posterior distribution, we use a variational inference approach that approximates the posterior with a member of the same exponential family as the prior, such that it minimizes a Kullback-Leibler criterion. On the

SUBMITTER: Tsilifis P 

PROVIDER: S-EPMC6189593 | biostudies-literature | 2018 Sep

REPOSITORIES: biostudies-literature