Siam-Asa Journal on Uncertainty Quantification最新文献_第3页

Leveraging Joint Sparsity in Hierarchical Bayesian Learning 利用层次贝叶斯学习中的联合稀疏性

IF 2 3区工程技术

Siam-Asa Journal on Uncertainty Quantification Pub Date : 2024-05-24 DOI: 10.1137/23m156255x

Jan Glaubitz, Anne Gelb

引用次数: 0

Ensemble Kalman Filters with Resampling 带有重采样功能的集合卡尔曼滤波器

IF 2 3区工程技术

Siam-Asa Journal on Uncertainty Quantification Pub Date : 2024-05-23 DOI: 10.1137/23m1594935

Omar Al-Ghattas, Jiajun Bao, Daniel Sanz-Alonso

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Nonparametric Estimation for Independent and Identically Distributed Stochastic Differential Equations with Space-Time Dependent Coefficients 具有时空相关系数的独立同分布随机微分方程的非参数估计

IF 2 3区工程技术

Siam-Asa Journal on Uncertainty Quantification Pub Date : 2024-05-21 DOI: 10.1137/23m1581662

Fabienne Comte, Valentine Genon-Catalot

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Wavelet-Based Density Estimation for Persistent Homology 基于小波的持久同源性密度估计

IF 2 3区工程技术

Siam-Asa Journal on Uncertainty Quantification Pub Date : 2024-04-18 DOI: 10.1137/23m1573811

Konstantin Häberle, Barbara Bravi, Anthea Monod

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Nonasymptotic Bounds for Suboptimal Importance Sampling 次优重要性取样的非渐近边界

IF 2 3区工程技术

Siam-Asa Journal on Uncertainty Quantification Pub Date : 2024-04-15 DOI: 10.1137/21m1427760

Carsten Hartmann, Lorenz Richter

{"title":"Nonasymptotic Bounds for Suboptimal Importance Sampling","authors":"Carsten Hartmann, Lorenz Richter","doi":"10.1137/21m1427760","DOIUrl":"https://doi.org/10.1137/21m1427760","url":null,"abstract":"SIAM/ASA Journal on Uncertainty Quantification, Volume 12, Issue 2, Page 309-346, June 2024. <br/> Abstract. Importance sampling is a popular variance reduction method for Monte Carlo estimation, where an evident question is how to design good proposal distributions. While in most cases optimal (zero-variance) estimators are theoretically possible, in practice only suboptimal proposal distributions are available and it can often be observed numerically that those can reduce statistical performance significantly, leading to large relative errors and therefore counteracting the original intention. Previous analysis on importance sampling has often focused on asymptotic arguments that work well in a large deviations regime. In this article, we provide lower and upper bounds on the relative error in a nonasymptotic setting. They depend on the deviation of the actual proposal from optimality, and we thus identify potential robustness issues that importance sampling may have, especially in high dimensions. We particularly focus on path sampling problems for diffusion processes with nonvanishing noise, for which generating good proposals comes with additional technical challenges. We provide numerous numerical examples that support our findings and demonstrate the applicability of the derived bounds.","PeriodicalId":56064,"journal":{"name":"Siam-Asa Journal on Uncertainty Quantification","volume":null,"pages":null},"PeriodicalIF":2.0,"publicationDate":"2024-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140562196","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Computing Statistical Moments Via Tensorization of Polynomial Chaos Expansions 通过多项式混沌扩展张量计算统计矩

IF 2 3区工程技术

Siam-Asa Journal on Uncertainty Quantification Pub Date : 2024-04-15 DOI: 10.1137/23m155428x

Rafael Ballester-Ripoll

引用次数: 0

Calculation of Epidemic First Passage and Peak Time Probability Distributions 流行病首次传播和高峰时间概率分布的计算

IF 2 3区工程技术

Siam-Asa Journal on Uncertainty Quantification Pub Date : 2024-04-04 DOI: 10.1137/23m1548049

Jacob Curran-Sebastian, Lorenzo Pellis, Ian Hall, Thomas House

{"title":"Calculation of Epidemic First Passage and Peak Time Probability Distributions","authors":"Jacob Curran-Sebastian, Lorenzo Pellis, Ian Hall, Thomas House","doi":"10.1137/23m1548049","DOIUrl":"https://doi.org/10.1137/23m1548049","url":null,"abstract":"SIAM/ASA Journal on Uncertainty Quantification, Volume 12, Issue 2, Page 242-261, June 2024. <br/> Abstract. Understanding the timing of the peak of a disease outbreak forms an important part of epidemic forecasting. In many cases, such information is essential for planning increased hospital bed demand and for designing of public health interventions. The time taken for an outbreak to become large is inherently stochastic and, therefore, uncertain, but after a sufficient number of infections has been reached the subsequent dynamics can be modeled accurately using ordinary differential equations. Here, we present analytical and numerical methods for approximating the time at which a stochastic model of a disease outbreak reaches a large number of cases and for quantifying the uncertainty arising from demographic stochasticity around that time. We then project this uncertainty forwards in time using an ordinary differential equation model in order to obtain a distribution for the peak timing of the epidemic that agrees closely with large simulations but that, for error tolerances relevant to most realistic applications, requires a fraction of the computational cost of full Monte Carlo approaches.","PeriodicalId":56064,"journal":{"name":"Siam-Asa Journal on Uncertainty Quantification","volume":null,"pages":null},"PeriodicalIF":2.0,"publicationDate":"2024-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140562019","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Method of Moments Estimator for Interacting Particle Systems and their Mean Field Limit 相互作用粒子系统及其平均场极限的矩估计方法

IF 2 3区工程技术

Siam-Asa Journal on Uncertainty Quantification Pub Date : 2024-04-04 DOI: 10.1137/22m153848x

Grigorios A. Pavliotis, Andrea Zanoni

引用次数: 0

Subsampling of Parametric Models with Bifidelity Boosting 利用双保真度提升对参数模型进行子采样

IF 2 3区工程技术

Siam-Asa Journal on Uncertainty Quantification Pub Date : 2024-04-04 DOI: 10.1137/22m1524989

Nuojin Cheng, Osman Asif Malik, Yiming Xu, Stephen Becker, Alireza Doostan, Akil Narayan

{"title":"Subsampling of Parametric Models with Bifidelity Boosting","authors":"Nuojin Cheng, Osman Asif Malik, Yiming Xu, Stephen Becker, Alireza Doostan, Akil Narayan","doi":"10.1137/22m1524989","DOIUrl":"https://doi.org/10.1137/22m1524989","url":null,"abstract":"SIAM/ASA Journal on Uncertainty Quantification, Volume 12, Issue 2, Page 213-241, June 2024. <br/>Abstract.Least squares regression is a ubiquitous tool for building emulators (a.k.a. surrogate models) of problems across science and engineering for purposes such as design space exploration and uncertainty quantification. When the regression data are generated using an experimental design process (e.g., a quadrature grid) involving computationally expensive models, or when the data size is large, sketching techniques have shown promise at reducing the cost of the construction of the regression model while ensuring accuracy comparable to that of the full data. However, random sketching strategies, such as those based on leverage scores, lead to regression errors that are random and may exhibit large variability. To mitigate this issue, we present a novel boosting approach that leverages cheaper, lower-fidelity data of the problem at hand to identify the best sketch among a set of candidate sketches. This in turn specifies the sketch of the intended high-fidelity model and the associated data. We provide theoretical analyses of this bifidelity boosting (BFB) approach and discuss the conditions the low- and high-fidelity data must satisfy for a successful boosting. In doing so, we derive a bound on the residual norm of the BFB sketched solution relating it to its ideal, but computationally expensive, high-fidelity boosted counterpart. Empirical results on both manufactured and PDE data corroborate the theoretical analyses and illustrate the efficacy of the BFB solution in reducing the regression error, as compared to the nonboosted solution.","PeriodicalId":56064,"journal":{"name":"Siam-Asa Journal on Uncertainty Quantification","volume":null,"pages":null},"PeriodicalIF":2.0,"publicationDate":"2024-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140562524","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Corrigendum: Quasi–Monte Carlo Finite Element Analysis for Wave Propagation in Heterogeneous Random Media 更正：异质随机介质中波传播的准蒙特卡罗有限元分析

IF 2 3区工程技术

Siam-Asa Journal on Uncertainty Quantification Pub Date : 2024-03-29 DOI: 10.1137/23m1624609

M. Ganesh, Frances Y. Kuo, Ian H. Sloan

引用次数: 0