Rare Event Simulation using Monte Carlo Methods

By G. Rubino, B. Tuffin. 2009

425 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Recent developments in GEANT4 (2016, background): “There is a large variety of biasing techniques, but they rely mostly on two main approaches: splitting and killing (SK) [231,233] and importance sampling (IS) [232,233].”
The future of risk assessment (2018, background): “CRs correspond to very small domains that are very hard to find in the large space of input configurations [34,165].”
A Cross-Entropy-Based Three-Stage Sequential Importance Sampling for Composite Power System Short-Term Reliability Evaluation (2013, background): “Based on the above conditions, likelihood for the sampled [21] can be expressed as follows:”
VALUATION : A N ADVERSARIAL APPROACH TO UNCOVER CATASTROPHIC FAILURES (2018, positive): “We draw upon a rich literature on rare event probability estimation (e.g., Bucklew 2004; Rubino & Tufﬁn 2009; Rubinstein & Kroese 2017).”
Extreme Value Analysis: an Introduction (2013, methodology): “For more details of the simulation of rare events, we suggest consulting Doucet et al. (2001), Bucklew (2011) and Rubino and Tufﬁn (2009).”
Three principles of data science: predictability, computability, and stability (PCS) (2018, background): “In fact, there is a specialized area of importance sampling to deal with simulating small probabilities (48, 49), but these ideas have not been widely adopted in practice.”