BIMC: The Bayesian Inverse Monte Carlo method for goal-oriented uncertainty quantification. Part II

Understand

In Part I (arXiv:1911.00619) of this article, we proposed an importance sampling algorithm to compute rare-event probabilities in forward uncertainty quantification problems.

• The algorithm, which we termed the "Bayesian Inverse Monte Carlo (BIMC) method", was shown to be optimal for problems in which the input-output operator is nearly linear.
• But applying the original BIMC to highly nonlinear systems can lead to several different failure modes.
• In this paper, we modify the BIMC method to extend its applicability to a wider class of systems.