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...

Feature subset selection has become more and more a common topic of research. This popularity is partly due to the growth in the number of features and application domains. It is of the greatest importance to take the most of every evaluation of the inducer, which is normally the more costly part. In this paper, a technique is proposed that takes into account the inducer evaluation both in the ...

Let A be a zero-sum free subset of Zn with |A| = k. We compute for k ≤ 7 the least possible size of the set of all subset-sums of A.