.png){kind=icon}
返回
Generalized Multiple Importance Sampling
.png){kind=icon}
OA
AI
DOI：10.1214/18-STS668.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Importance sampling (IS) methods are broadly used to approximate posterior distributions or their moments. In the standard IS approach, samples are drawn from a single proposal distribution and weighted adequately. However, since the performance in IS depends on the mismatch between the targeted and the proposal distributions, several proposal densities are often employed for the generation of samples. Under this multiple importance sampling (MIS) scenario, extensive literature has addressed the selection and adaptation of the proposal distributions, interpreting the sampling and weighting steps in different ways. In this paper, we establish a novel general framework with sampling and weighting procedures when more than one proposal is available. The new framework encompasses most relevant MIS schemes in the literature, and novel valid schemes appear naturally. All the MIS schemes are compared and ranked in terms of the variance of the associated estimators. Finally, we provide illustrative examples revealing that, even with a good choice of the proposal densities, a careful interpretation of the sampling and weighting procedures can make a significant difference in the performance of the method.
Keyword:
Monte Carlo methods
multiple importance sampling
Bayesian inference
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
3.4
论文数:
1.0K
被引数:
8.7K
机构
引用论文
Catalyst free-click polymerization: A versatile method for the preparation of soybean oil based poly1,2,3-triazoles as coatings with efficient biocidal activity and excellent cytocompatibility催化剂自由点击聚合: 一种制备大豆油基聚1，2，3-三唑作为具有高效杀生物活性和优异细胞相容性的涂层的通用方法.png)
Polymer
PolymerIF0
PREAI
分享收藏
查看更多内容