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

• Main Authors: Bachoc Francois, Bachouch Achref, Lenôtre Lionel
• Format: Article
• Language: English
• Published: EDP Sciences 2015-01-01
• Series: ESAIM: Proceedings and Surveys
• Online Access: http://dx.doi.org/10.1051/proc/201448013

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.