A generalized numerical framework is presented for constructing computational models capable of processing inputs defined as sets of probability distribution functions and sets of intervals. The framework implements a novel solution strategy that couples advanced sampling-based methods and optimization procedures, and provides a credible tool for calculating imprecise measure of failure probabi...

Structural elements, such as stiffened panels, are designed by combining material strength data obtained from coupon tests with a failure theory for 3D stress field. Material variability is captured by dozens of coupon tests, but there remains epistemic uncertainty due to error in the failure theory, which can be reduced by element tests. Conservativeness to compensate for the uncertainty in fa...

Two basic formulas, for the mean and variance of the number of effects in an epidemiological cohort, are derived. The formula for variance shows “extra-binomial variation” or “overdispersion” when there is correlated uncertainty of the probability of an effect. The formulas were validated by a numerical Monte Carlo study. The method of including “epistemic” uncertainty discussed by Hofer (E. Ho...

In this work, we demonstrate how to reliably estimate epistemic uncertainty while maintaining the flexibility needed capture complicated aleatoric distributions. To end, propose an ensemble of Normalizing Flows (NF), which are state-of-the-art in modeling uncertainty. The ensembles created via sets fixed dropout masks, making them less expensive than creating separate NF models. We leverage uni...

In previous papers, we introduced a normative scheme for scene construction and epistemic (visual) searches based upon active inference. This scheme provides a principled account of how people decide where to look, when categorising a visual scene based on its contents. In this paper, we use active inference to explain the visual searches of normal human subjects; enabling us to answer some key...