{"title":"通过平均场近似估计的期望值精度为1/ n:扩展摘要","authors":"Nicolas Gast","doi":"10.1145/3078505.3078523","DOIUrl":null,"url":null,"abstract":"In this paper, we study the accuracy of mean-field approximation. We show that, under general conditions, the expectation of any performance functional converges at rate O(1/N) to its mean-field approximation. Our result applies for finite and infinite-dimensional mean-field models. We provide numerical experiments that demonstrate that this rate of convergence is tight.","PeriodicalId":133673,"journal":{"name":"Proceedings of the 2017 ACM SIGMETRICS / International Conference on Measurement and Modeling of Computer Systems","volume":"40 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2017-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"7","resultStr":"{\"title\":\"Expected Values Estimated via Mean-Field Approximation are 1/N-Accurate: Extended Abstract\",\"authors\":\"Nicolas Gast\",\"doi\":\"10.1145/3078505.3078523\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this paper, we study the accuracy of mean-field approximation. We show that, under general conditions, the expectation of any performance functional converges at rate O(1/N) to its mean-field approximation. Our result applies for finite and infinite-dimensional mean-field models. We provide numerical experiments that demonstrate that this rate of convergence is tight.\",\"PeriodicalId\":133673,\"journal\":{\"name\":\"Proceedings of the 2017 ACM SIGMETRICS / International Conference on Measurement and Modeling of Computer Systems\",\"volume\":\"40 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2017-06-05\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"7\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the 2017 ACM SIGMETRICS / International Conference on Measurement and Modeling of Computer Systems\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/3078505.3078523\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 2017 ACM SIGMETRICS / International Conference on Measurement and Modeling of Computer Systems","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3078505.3078523","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Expected Values Estimated via Mean-Field Approximation are 1/N-Accurate: Extended Abstract
In this paper, we study the accuracy of mean-field approximation. We show that, under general conditions, the expectation of any performance functional converges at rate O(1/N) to its mean-field approximation. Our result applies for finite and infinite-dimensional mean-field models. We provide numerical experiments that demonstrate that this rate of convergence is tight.
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