Abstract
Improving Importance Sampling estimators for rare event probabilities requires sharp approximations of the optimal density leading to a nearly zero-variance estimator. This paper presents a new way to handle the estimation of the probability of a rare event defined by the fact that the empirical mean of summands of a random walk belongs to a measurable set. We provide a sharp approximation of the optimal conditional density on long runs.
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Caron, V. (2014). Importance Sampling for Multi-Constraints Rare Event Probability. In: Melas, V., Mignani, S., Monari, P., Salmaso, L. (eds) Topics in Statistical Simulation. Springer Proceedings in Mathematics & Statistics, vol 114. Springer, New York, NY. https://doi.org/10.1007/978-1-4939-2104-1_11
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DOI: https://doi.org/10.1007/978-1-4939-2104-1_11
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