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An algorithm for rare-event probability estimation using the product rule of probability theory
| Content Provider | Semantic Scholar |
|---|---|
| Author | Botev, Zdravko I. |
| Copyright Year | 2008 |
| Abstract | Although importance sampling is an established and effective sampling and estimation technique, it becomes unstable and unreliable for high- dimensional problems. The main reason is that the likelihood ratio in the importance sampling estimator degenerates when the dimension of the problem becomes large. Various remedies to this problem have been suggested, including heuristics such as resampling. Even so, the consen- sus is that for large-dimensional problems, likelihood ratios (and hence importance sampling) should be avoided. In this paper we introduce a new adaptive simulation approach that does away with likelihood ratios, while retaining the multi-level approach of the cross-entropy method. Like the latter, the method can be used for rare-event probability estimation, optimization, and counting. Moreover, the method allows one to sam- ple from the target distribution rather than asymptotically as in Markov chain Monte Carlo. Numerical examples demonstrate the effectiveness of the method. |
| File Format | PDF HTM / HTML |
| Alternate Webpage(s) | http://espace.library.uq.edu.au/view/UQ:151299/ADAM_algorithm.pdf |
| Language | English |
| Access Restriction | Open |
| Content Type | Text |
| Resource Type | Article |
