Large deviations in rare events simulation: examples, counterexamples and alternatives
(2002) Proceedings of Fourth International Conference on Monte Carlo and Quasi-Monte Carlo Methods in Scientific Computing p.1-9- Abstract
- When simulating small probabilities, say of order 10<sup>-6</sup> or less, by importance sampling, an established principle is to choose the importance sampling distribution as close to the conditional distribution given the rare event as possible. Implementing this often leads into large deviations calculations and exponential change of measure. We survey some of the standard examples where this approach works and supplement existing counterexamples with new ones. Difficulties often arise as consequence of reflecting barriers and we present an algorithm which at least in simple cases is able to deal with this problem. Also the case of heavy-tailed distributions is considered
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/611224
- author
- Asmussen, Sören LU
- organization
- publishing date
- 2002
- type
- Chapter in Book/Report/Conference proceeding
- publication status
- published
- subject
- keywords
- conditional distribution, heavy-tailed distributions, importance sampling, rare event, small probabilities, rare events simulation
- host publication
- Monte-Carlo and Quasi-Monte Carlo Methods 2000. Proceedings of a Conference
- pages
- 1 - 9
- publisher
- Springer
- conference name
- Proceedings of Fourth International Conference on Monte Carlo and Quasi-Monte Carlo Methods in Scientific Computing
- conference location
- Hong Kong, China
- conference dates
- 2000-11-27 - 2000-12-01
- external identifiers
-
- wos:000175526000001
- ISBN
- 3-540-42718-X
- language
- English
- LU publication?
- yes
- id
- 5ac57197-1d73-417a-9ea2-2fe7392cf67e (old id 611224)
- date added to LUP
- 2016-04-04 10:47:27
- date last changed
- 2018-11-21 21:00:47
@inproceedings{5ac57197-1d73-417a-9ea2-2fe7392cf67e, abstract = {{When simulating small probabilities, say of order 10<sup>-6</sup> or less, by importance sampling, an established principle is to choose the importance sampling distribution as close to the conditional distribution given the rare event as possible. Implementing this often leads into large deviations calculations and exponential change of measure. We survey some of the standard examples where this approach works and supplement existing counterexamples with new ones. Difficulties often arise as consequence of reflecting barriers and we present an algorithm which at least in simple cases is able to deal with this problem. Also the case of heavy-tailed distributions is considered}}, author = {{Asmussen, Sören}}, booktitle = {{Monte-Carlo and Quasi-Monte Carlo Methods 2000. Proceedings of a Conference}}, isbn = {{3-540-42718-X}}, keywords = {{conditional distribution; heavy-tailed distributions; importance sampling; rare event; small probabilities; rare events simulation}}, language = {{eng}}, pages = {{1--9}}, publisher = {{Springer}}, title = {{Large deviations in rare events simulation: examples, counterexamples and alternatives}}, year = {{2002}}, }