Introduction to Rare Event Simulation

By G. Rubino, B. Tuffin. Rare Event Simulation using Monte Carlo Methods, 2009

390 citations recorded by the source corpus.

Subfields: Demography, Management Science and Operations Research, Mathematical Physics

View Paper: https://www.semanticscholar.org/paper/bc306b61c77b0a1008e7f1ebcf3c3ee4bd0e3d12

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Citations in the corpus

Accelerated Evaluation of Automated Vehicles Safety in Lane-Change Scenarios Based on Importance Sampling Techniques (2016, background): “General overviews about IS can be found in [54]–[56].”
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).”
Nonequilibrium Markov Processes Conditioned on Large Deviations (2014, methodology): “Rare Event Simulations: Many numerical methods used for determining rare event probabilities are based on the idea of importance sampling, whereby the underlying distribution P of a random variable or process is modified to a target distribution Q putting more weight on the rare events to be sampled”
VALUATION : A N ADVERSARIAL APPROACH TO UNCOVER CATASTROPHIC FAILURES (2018, positive): “The failure probability estimation method uses importance sampling (IS) (e.g., Section 4.2, Bucklew 2004) where the distribution P X is replaced by a proposal distribution Q .”
Sharp asymptotic and finite-sample rates of convergence of empirical measures in Wasserstein distance (2017, methodology): “In practice, a technique known as importance sampling [9] is often used to reduce the variance of estimates produced on the basis of random samples from a distribution.”
State-dependent importance sampling for regularly varying random walks (2008, background): “Keywords: State dependent; importance sampling; regularly varying; random walk; heavy tail; strong efficiency; rare-event simulation 2000 Mathematics Subject Classification: Primary 60G50; 60J05; 68W40 Secondary 60G70”
Efficient rare-event simulation for the maximum of heavy-tailed random walks (2008, background): “mplies that the number of replications required to estimate P(M >b) with a given relative accuracy grows at rate o(|logP(M >b)|); see Asmussen and Glynn (2007), Juneja and Shahabuddin (2006) or Bucklew (2004), Section 5.2, for a discussion of eﬃciency in rare-event simulation. A strongly eﬃcie”