Foundations of data science (Springfield, Mo.)最新文献_第4页

The rankability of weighted data from pairwise comparisons 两两比较中加权数据的排名

Foundations of data science (Springfield, Mo.) Pub Date : 2021-01-01 DOI: 10.3934/FODS.2021002

Paul E. Anderson, T. Chartier, A. Langville, Kathryn E. Pedings-Behling

引用次数: 10

Learning landmark geodesics using the ensemble Kalman filter 使用集合卡尔曼滤波器学习地标测地线

Foundations of data science (Springfield, Mo.) Pub Date : 2021-01-01 DOI: 10.3934/fods.2021020

Andreas Bock, C. Cotter

引用次数: 2

Reconstructing linearly embedded graphs: A first step to stratified space learning 重构线性嵌入图:分层空间学习的第一步

Foundations of data science (Springfield, Mo.) Pub Date : 2021-01-01 DOI: 10.3934/fods.2021026

Yossi Bokor Bleile, Katharine Turner, Christopher Williams

引用次数: 1

Score matching filters for Gaussian Markov random fields with a linear model of the precision matrix 分数匹配滤波器高斯马尔可夫随机场与精度矩阵的线性模型

Foundations of data science (Springfield, Mo.) Pub Date : 2021-01-01 DOI: 10.3934/fods.2021030

Marie Turčičová, J. Mandel, K. Eben

引用次数: 1

An international initiative of predicting the SARS-CoV-2 pandemic using ensemble data assimilation 利用集合数据同化预测SARS-CoV-2大流行的国际倡议

Foundations of data science (Springfield, Mo.) Pub Date : 2020-12-11 DOI: 10.3934/fods.2021001

G. Evensen, Javier Amezcua, M. Bocquet, A. Carrassi, A. Farchi, A. Fowler, P. Houtekamer, C. Jones, R. Moraes, M. Pulido, C. Sampson, F. Vossepoel

{"title":"An international initiative of predicting the SARS-CoV-2 pandemic using ensemble data assimilation","authors":"G. Evensen, Javier Amezcua, M. Bocquet, A. Carrassi, A. Farchi, A. Fowler, P. Houtekamer, C. Jones, R. Moraes, M. Pulido, C. Sampson, F. Vossepoel","doi":"10.3934/fods.2021001","DOIUrl":"https://doi.org/10.3934/fods.2021001","url":null,"abstract":"This work demonstrates the efficiency of using iterative ensemble smoothers to estimate the parameters of an SEIR model. We have extended a standard SEIR model with age-classes and compartments of sick, hospitalized, and dead. The data conditioned on are the daily numbers of accumulated deaths and the number of hospitalized. Also, it is possible to condition the model on the number of cases obtained from testing. We start from a wide prior distribution for the model parameters; then, the ensemble conditioning leads to a posterior ensemble of estimated parameters yielding model predictions in close agreement with the observations. The updated ensemble of model simulations has predictive capabilities and include uncertainty estimates. In \u0000particular, we estimate the effective reproductive number as a function of time, and we can assess the impact of different intervention measures. By starting from the updated set of model parameters, we can make accurate short-term predictions of the epidemic development assuming \u0000knowledge of the future effective reproductive number. Also, the model system allows for the computation of long-term scenarios of the epidemic under different assumptions. We have applied the model system on data sets from several countries, i.e., the four European countries Norway, England, The Netherlands, and France; the province of Quebec in Canada; the South American countries Argentina and Brazil; and the four US states Alabama, North Carolina, California, and New York. These countries and states all have vastly different developments of the epidemic, and we could accurately model the SARS-CoV-2 outbreak in all of them. We realize that more complex models, e.g., with regional compartments, may be desirable, and we suggest that the approach used here should be applicable also for these models.","PeriodicalId":73054,"journal":{"name":"Foundations of data science (Springfield, Mo.)","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43519659","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 15

A surrogate-based approach to nonlinear, non-Gaussian joint state-parameter data assimilation 一种基于代理的非线性非高斯联合状态参数数据同化方法

Foundations of data science (Springfield, Mo.) Pub Date : 2020-12-08 DOI: 10.3934/fods.2021019

J. Maclean, E. Spiller

引用次数: 1

Estimating linear response statistics using orthogonal polynomials: An rkhs formulation 估计线性响应统计使用正交多项式:一个rkhs公式

Foundations of data science (Springfield, Mo.) Pub Date : 2020-12-08 DOI: 10.3934/fods.2020021

He Zhang, J. Harlim, Xiantao Li

{"title":"Estimating linear response statistics using orthogonal polynomials: An rkhs formulation","authors":"He Zhang, J. Harlim, Xiantao Li","doi":"10.3934/fods.2020021","DOIUrl":"https://doi.org/10.3934/fods.2020021","url":null,"abstract":"We study the problem of estimating linear response statistics under external perturbations using time series of unperturbed dynamics. Based on the fluctuation-dissipation theory, this problem is reformulated as an unsupervised learning task of estimating a density function. We consider a nonparametric density estimator formulated by the kernel embedding of distributions with \"Mercer-type\" kernels, constructed based on the classical orthogonal polynomials defined on non-compact domains. While the resulting representation is analogous to Polynomial Chaos Expansion (PCE), the connection to the reproducing kernel Hilbert space (RKHS) theory allows one to establish the uniform convergence of the estimator and to systematically address a practical question of identifying the PCE basis for a consistent estimation. We also provide practical conditions for the well-posedness of not only the estimator but also of the underlying response statistics. Finally, we provide a statistical error bound for the density estimation that accounts for the Monte-Carlo averaging over non-i.i.d time series and the biases due to a finite basis truncation. This error bound provides a means to understand the feasibility as well as limitation of the kernel embedding with Mercer-type kernels. Numerically, we verify the effectiveness of the estimator on two stochastic dynamics with known, yet, non-trivial equilibrium densities.","PeriodicalId":73054,"journal":{"name":"Foundations of data science (Springfield, Mo.)","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48255540","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 8

ANAPT: Additive noise analysis for persistence thresholding 持久性阈值的加性噪声分析

Foundations of data science (Springfield, Mo.) Pub Date : 2020-12-07 DOI: 10.3934/fods.2022005

Audun D. Myers, Firas A. Khasawneh, Brittany Terese Fasy

引用次数: 1

Mean field limit of Ensemble Square Root filters - discrete and continuous time 集合平方根滤波器的平均场极限-离散和连续时间

Foundations of data science (Springfield, Mo.) Pub Date : 2020-11-20 DOI: 10.3934/FODS.2021003

Theresa Lange, W. Stannat

引用次数: 15

Feedback particle filter for collective inference 用于集体推理的反馈粒子滤波器

Foundations of data science (Springfield, Mo.) Pub Date : 2020-10-13 DOI: 10.3934/fods.2021018

Jin W. Kim, P. Mehta

{"title":"Feedback particle filter for collective inference","authors":"Jin W. Kim, P. Mehta","doi":"10.3934/fods.2021018","DOIUrl":"https://doi.org/10.3934/fods.2021018","url":null,"abstract":"<p style='text-indent:20px;'>The purpose of this paper is to describe the feedback particle filter algorithm for problems where there are a large number (<inline-formula><tex-math id=\"M1\">begin{document}$ M $end{document}</tex-math></inline-formula>) of non-interacting agents (targets) with a large number (<inline-formula><tex-math id=\"M2\">begin{document}$ M $end{document}</tex-math></inline-formula>) of non-agent specific observations (measurements) that originate from these agents. In its basic form, the problem is characterized by data association uncertainty whereby the association between the observations and agents must be deduced in addition to the agent state. In this paper, the large-<inline-formula><tex-math id=\"M3\">begin{document}$ M $end{document}</tex-math></inline-formula> limit is interpreted as a problem of collective inference. This viewpoint is used to derive the equation for the empirical distribution of the hidden agent states. A feedback particle filter (FPF) algorithm for this problem is presented and illustrated via numerical simulations. Results are presented for the Euclidean and the finite state-space cases, both in continuous-time settings. The classical FPF algorithm is shown to be the special case (with <inline-formula><tex-math id=\"M4\">begin{document}$ M = 1 $end{document}</tex-math></inline-formula>) of these more general results. The simulations help show that the algorithm well approximates the empirical distribution of the hidden states for large <inline-formula><tex-math id=\"M5\">begin{document}$ M $end{document}</tex-math></inline-formula>.</p>","PeriodicalId":73054,"journal":{"name":"Foundations of data science (Springfield, Mo.)","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46470057","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 4