The objective of this work is to quantify the uncertainty in probability failure estimates resulting from incomplete knowledge distributions for input random variables. We propose a framework that couples widely used Subset simulation (SuS) with Bayesian/information theoretic multi-model inference. process starts data infer model inputs. Often such sets are small. Multi-model inference assess a...

A finite automaton can be either deterministic (DFA) or nondeterministic (NFA). An automaton-based task is in general more efficient when performed with a DFA rather than an NFA. For any NFA there equivalent that generated by the classical Subset Construction algorithm. When, however, large may transformed into series of actions operating directly on NFA, unnecessarily expensive computation, as...