Importance Sampling for multi-constraints rare event probability
Abstract
Improving Importance Sampling estimators for rare event probabilities requires sharp approx- imations 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 as a finite intersection of subset. We provide a sharp approximation of the density of long runs of a random walk condi- tioned by multiples constraints, each of them defined by an average of a function of its summands as their number tends to infinity.
- Publication:
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arXiv e-prints
- Pub Date:
- January 2014
- DOI:
- 10.48550/arXiv.1401.3257
- arXiv:
- arXiv:1401.3257
- Bibcode:
- 2014arXiv1401.3257C
- Keywords:
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- Mathematics - Statistics Theory
- E-Print:
- Conference paper