{"title":"随机微分方程的对称重要抽样","authors":"Andrew B. Leach, Kevin K. Lin, M. Morzfeld","doi":"10.2140/camcos.2018.13.215","DOIUrl":null,"url":null,"abstract":"We study a class of importance sampling methods for stochastic differential equations (SDEs). A small-noise analysis is performed, and the results suggest that a simple symmetrization procedure can significantly improve the performance of our importance sampling schemes when the noise is not too large. We demonstrate that this is indeed the case for a number of linear and nonlinear examples. Potential applications, e.g., data assimilation, are discussed.","PeriodicalId":49265,"journal":{"name":"Communications in Applied Mathematics and Computational Science","volume":"53 1","pages":""},"PeriodicalIF":1.9000,"publicationDate":"2017-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Symmetrized importance samplers for stochastic differential equations\",\"authors\":\"Andrew B. Leach, Kevin K. Lin, M. Morzfeld\",\"doi\":\"10.2140/camcos.2018.13.215\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"We study a class of importance sampling methods for stochastic differential equations (SDEs). A small-noise analysis is performed, and the results suggest that a simple symmetrization procedure can significantly improve the performance of our importance sampling schemes when the noise is not too large. We demonstrate that this is indeed the case for a number of linear and nonlinear examples. Potential applications, e.g., data assimilation, are discussed.\",\"PeriodicalId\":49265,\"journal\":{\"name\":\"Communications in Applied Mathematics and Computational Science\",\"volume\":\"53 1\",\"pages\":\"\"},\"PeriodicalIF\":1.9000,\"publicationDate\":\"2017-07-10\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Communications in Applied Mathematics and Computational Science\",\"FirstCategoryId\":\"100\",\"ListUrlMain\":\"https://doi.org/10.2140/camcos.2018.13.215\",\"RegionNum\":3,\"RegionCategory\":\"数学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"MATHEMATICS, APPLIED\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Communications in Applied Mathematics and Computational Science","FirstCategoryId":"100","ListUrlMain":"https://doi.org/10.2140/camcos.2018.13.215","RegionNum":3,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"MATHEMATICS, APPLIED","Score":null,"Total":0}
Symmetrized importance samplers for stochastic differential equations
We study a class of importance sampling methods for stochastic differential equations (SDEs). A small-noise analysis is performed, and the results suggest that a simple symmetrization procedure can significantly improve the performance of our importance sampling schemes when the noise is not too large. We demonstrate that this is indeed the case for a number of linear and nonlinear examples. Potential applications, e.g., data assimilation, are discussed.
期刊介绍：
CAMCoS accepts innovative papers in all areas where mathematics and applications interact. In particular, the journal welcomes papers where an idea is followed from beginning to end — from an abstract beginning to a piece of software, or from a computational observation to a mathematical theory.
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