Bayesian estimation of probabilistic sensitivity measures for computer experiments

Simulation-based experiments have become increasingly important for risk evaluation and decision-making in a broad range of applications, in engineering, science and public policy. In the presence of uncertainty regarding the phenomenon under study and, in particular, of the simulation model inputs, a probabilistic approach to sensitivity analysis becomes crucial. A number of global sensitivity measures have been proposed in the literature, together with estimation methods designed to work at relatively low computational costs. First in line is the one-sample or given-data approach which relies on adequate partitions of the input space. We propose a Bayesian alternative for the estimation of several sensitivity measures which shows a good performance on synthetic examples, specially for small sample sizes. Furthermore, we propose the use of a nonparametric approach for conditional density estimation which bypasses the need for pre-defined partitions, allowing the sharing of information across the entire input space through the underlying assumption of partial exchangeability. In both cases, the Bayesian paradigm ensures the quantification of the uncertainty in the estimation.
Coauthors: Emanuele Borgonovo and  Xuefei Lu

Talk in english.