Sentence examples for rare probability from inspiring English sources

Here we extend the capability of the method to rare probability computation by using the idea of importance sampling (IS).

Even though the rare probability of this accident, it still has got a lot of concerns in the safety analyses of HTR-PM, due to the possible severe consequences of the air ingress accident, which would cause oxidation of the graphite material in the reactor.

Nonetheless, excision of a transposon is estimated to be rare (probability less than 1 × 10−4 transpositions), and the probability of an adverse event is even lower [51], [52].

These risks, and their consequences, however, ultimately became visible when those rare probabilities occurred.

In this paper, we present an efficient numerical method for evaluating rare failure probability.

In this article, we develop a Kriging-based adaptive Importance Sampling approach for rare event probability estimation.

Typical probabilistic problems in an engineering context include rare event probability estimation for physical models where spatial autocorrelation of material property parameters is significant.

We demonstrate that, by combining with the CE method, a surrogate-based IS algorithm can be constructed and is highly efficient for rare failure probability computation it incurs much reduced simulation efforts compared to the traditional CE-IS method.

Considering that the endogenous TCR recombinations observed in non-Tg single RAG-deficient mice are extremely rare, the probability that a productive rearrangements occur in both α and β TCR loci and in the same cell is likely too low to result in receptor expression.

It is important to note that from the evolutionary point of view, the transition in homologous proteins from cyclic tetrameric to cyclic pentameric states is a very rare (low probability) event [ 1].

In that case, some properties can be derived on the partial robustness of classical methods such as FORM, Monte Carlo or response surfaces to estimate rare failure probabilities under the constraint of the number of expensive model runs, such as robust probability bounds, saved number of model runs or guaranteed variance reduction.