Jose Blanchet, Jingchen Liu, and Peter Glynn. 2006. State-dependent Importance Sampling and large Deviations. In Proceedings of the 1st international conference on Performance evaluation methodolgies and tools (valuetools ’06). Association for Computing Machinery, New York, NY, USA, 20–es. https://doi.org/10.1145/1190095.1190120
Abstract
Large deviations analysis for light-tailed systems provides an asymptotic description of the optimal importance sampler in the scaling of the Law of Large Numbers. As we will show by means of a simple example related to computational finance, such asymptotic description can be interpreted indifferent ways suggesting several importance sampling algorithms, some of them state-dependent. In turn, the performance of the suggested algorithms can be substantially different.
Authors
Jose Blanchet, Jingchen Liu, Peter Glynn
Publication date
2006/10/11
Book
Proceedings of the 1st international conference on Performance evaluation methodolgies and tools
Pages
20-es
