期刊
INTERNATIONAL JOURNAL OF APPROXIMATE REASONING
卷 101, 期 -, 页码 31-48出版社
ELSEVIER SCIENCE INC
DOI: 10.1016/j.ijar.2018.06.009
关键词
Monte Carlo; Simulation; Estimation; Lower prevision; Imprecise probability; Importance sampling
资金
- H2020 Marie Curie ITN, UTOPIAE [722734]
We develop a theoretical framework for studying numerical estimation of lower previsions, generally applicable to two-level Monte Carlo methods, importance sampling methods, and a wide range of other sampling methods one might devise. We link consistency of these estimators to Glivenko-Cantelli classes, and for the sub-Gaussian case we show how the correlation structure of this process can be used to bound the bias and prove consistency. We also propose a new upper estimator, which can be used along with the standard lower estimator, in order to provide a simple confidence interval. As a case study of this framework, we then discuss how importance sampling can be exploited to provide accurate numerical estimates of lower previsions. We propose an iterative importance sampling method to drastically improve the performance of imprecise importance sampling. We demonstrate our results on the imprecise Dirichlet model. (C) 2018 Elsevier Inc. All rights reserved.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据