Bayesian Model Averaging for Reliability Analysis with Probability Distribution Model Form Uncertainty

Abstract

rst specify probability distribution models for all of the input random variables. In practice, these models are often estimated based on observed data, and doing so introduces uncertainty because the true underlying probability distributions are unknown. Recent work has shown that this uncertainty can be addressed by quantifying the amount of uncertainty present in the estimated distribution model parameters. However, such an approach still assumes that the form of the probability distribution model is known. In this paper, we present an approach that makes use of Bayesian model averaging to quantify uncertainty associated with both distribution model parameters and distribution model form. The proposed approach is demonstrated for the reliability analysis of a bistable MEMS device; we make use of the Ecient Global Reliability Analysis method to eciently propagate the distribution uncertainty through the reliability analysis.