Error Bounds for Dynamical Spectral Estimation.

IF 1.9 Q1 MATHEMATICS, APPLIED
SIAM journal on mathematics of data science Pub Date : 2021-01-01 Epub Date: 2021-02-11 DOI:10.1137/20m1335984
Robert J Webber, Erik H Thiede, Douglas Dow, Aaron R Dinner, Jonathan Weare
{"title":"Error Bounds for Dynamical Spectral Estimation.","authors":"Robert J Webber, Erik H Thiede, Douglas Dow, Aaron R Dinner, Jonathan Weare","doi":"10.1137/20m1335984","DOIUrl":null,"url":null,"abstract":"<p><p>Dynamical spectral estimation is a well-established numerical approach for estimating eigenvalues and eigenfunctions of the Markov transition operator from trajectory data. Although the approach has been widely applied in biomolecular simulations, its error properties remain poorly understood. Here we analyze the error of a dynamical spectral estimation method called \"the variational approach to conformational dynamics\" (VAC). We bound the approximation error and estimation error for VAC estimates. Our analysis establishes VAC's convergence properties and suggests new strategies for tuning VAC to improve accuracy.</p>","PeriodicalId":74797,"journal":{"name":"SIAM journal on mathematics of data science","volume":"3 1","pages":"225-252"},"PeriodicalIF":1.9000,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8336423/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"SIAM journal on mathematics of data science","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1137/20m1335984","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2021/2/11 0:00:00","PubModel":"Epub","JCR":"Q1","JCRName":"MATHEMATICS, APPLIED","Score":null,"Total":0}
引用次数: 0

Abstract

Dynamical spectral estimation is a well-established numerical approach for estimating eigenvalues and eigenfunctions of the Markov transition operator from trajectory data. Although the approach has been widely applied in biomolecular simulations, its error properties remain poorly understood. Here we analyze the error of a dynamical spectral estimation method called "the variational approach to conformational dynamics" (VAC). We bound the approximation error and estimation error for VAC estimates. Our analysis establishes VAC's convergence properties and suggests new strategies for tuning VAC to improve accuracy.

Abstract Image

Abstract Image

Abstract Image

动态频谱估算的误差限。
动态谱估计是从轨迹数据中估算马尔可夫转换算子特征值和特征函数的一种成熟的数值方法。虽然这种方法已广泛应用于生物分子模拟,但人们对其误差特性仍然知之甚少。在此,我们分析了一种名为 "构象动力学变分法"(VAC)的动态谱估计方法的误差。我们对 VAC 估计的近似误差和估计误差进行了约束。我们的分析确定了 VAC 的收敛特性,并提出了调整 VAC 以提高准确性的新策略。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信