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arXiv · 1411.5883

Hastings-Metropolis algorithm on Markov chains for small-probability estimation

François Bachoc·Lionel Lenôtre·Achref Bachouch
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Abstract

Shielding studies in neutron transport, with Monte Carlo codes, yield challenging problems of small-probability estimation. The particularity of these studies is that the small probability to estimate is formulated in terms of the distribution of a Markov chain, instead of that of a random vector in more classical cases. Thus, it is not straightforward to adapt classical statistical methods, for estimating small probabilities involving random vectors, to these neutron-transport problems. A recent interacting-particle method for small-probability estimation, relying on the Hastings-Metropolis algorithm, is presented. It is shown how to adapt the Hastings-Metropolis algorithm when dealing with Markov chains. A convergence result is also shown. Then, the practical implementation of the resulting method for small-probability estimation is treated in details, for a Monte Carlo shielding study. Finally, it is shown, for this study, that the proposed interacting-particle method considerably outperforms a simple-Monte Carlo method, when the probability to estimate is small.

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François Bachoc, Lionel Lenôtre, Achref Bachouch. 2014-11-21. Hastings-Metropolis algorithm on Markov chains for small-probability estimation. https://arxiv.org/abs/1411.5883

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