Erratum: “Prediction uncertainty and optimal experimental design for learning dynamical systems” [Chaos <b>26</b>, 063110 (2016)]

Letham, Benjamin; Letham, Portia A.; Rudin, Cynthia; Browne, Edward P.

doi:10.1063/1.4986799

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Design Of Experiments Model Selection and Numerical Implementation1

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2020

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“…Letham, Letham, Rudin, and Browne (2016) developed a method that uses prediction as a measure for testing the usefulness of carrying out additional experiments. They derived theoretical properties of their method (note the small correction presented in Letham, Letham, Rudin, and Browne (2017)) and applied it to real data. Shulkind, Horesh, and Avron (2018) considered a class of misspecified dynamic models and suggested a novel formulation for experimental design.…”

Section: Design Of Experiments Model Selection and Numerical Implementationmentioning

confidence: 99%

Differential equations in data analysis

Dattner

2020

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Differential equations have proven to be a powerful mathematical tool in science and engineering, leading to better understanding, prediction, and control of dynamic processes. In this paper, we review the role played by differential equations in data analysis. More specifically, we consider the intersection between differential equations and data analysis in the light of modern statistical learning methodologies. This article is categorized under: Data: Types and Structure > Time Series, Stochastic Processes, and Functional Data Statistical and Graphical Methods of Data Analysis > Nonparametric Methods Statistical Models > Nonlinear Models

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Section: Design Of Experiments Model Selection and Numerical Implementationmentioning

confidence: 99%

Differential equations in data analysis

Dattner

2020

WIREs Computational Stats

View full text Add to dashboard Cite

show abstract

Erratum: “Prediction uncertainty and optimal experimental design for learning dynamical systems” [Chaos 26, 063110 (2016)]

Cited by 1 publication

References 1 publication

Differential equations in data analysis

Differential equations in data analysis

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