A sequential Monte Carlo approach to computing tail probabilities in stochastic models

DOI10.1214/10-AAP758zbMath1246.60042arXiv1202.4582OpenAlexW3103555374MaRDI QIDQ657700

Tze Leung Lai, Hock Peng Chan

Publication date: 10 January 2012

Published in: The Annals of Applied Probability (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1202.4582

zbMATH Keywords

large deviationslogarithmic efficiencyexceedance probabilitiessequential importance sampling and resampling

Mathematics Subject Classification ID

Computational methods in Markov chains (60J22) Probabilistic models, generic numerical methods in probability and statistics (65C20) Monte Carlo methods (65C05) Interacting random processes; statistical mechanics type models; percolation theory (60K35) Large deviations (60F10)

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