Adaptive step-size selection for state-space probabilistic differential equation solvers

DOI10.1007/s11222-019-09899-5zbMath1436.62092OpenAlexW2976033080WikidataQ115380715 ScholiaQ115380715MaRDI QIDQ2302456

David A. Campbell, Oksana A. Chkrebtii

Publication date: 26 February 2020

Published in: Statistics and Computing (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s11222-019-09899-5

zbMATH Keywords

numerical methodsdifferential equationsdata assimilationuncertainty quantificationstatistical designBayesian statistical design

Mathematics Subject Classification ID

Optimal statistical designs (62K05) Bayesian inference (62F15) Monte Carlo methods (65C05) Numerical methods for initial value problems involving ordinary differential equations (65L05)

Related Items (5)

Randomised one-step time integration methods for deterministic operator differential equations ⋮ A probabilistic finite element method based on random meshes: a posteriori error estimators and Bayesian inverse problems ⋮ A modern retrospective on probabilistic numerics ⋮ A role for symmetry in the Bayesian solution of differential equations ⋮ Bayesian numerical methods for nonlinear partial differential equations

Cites Work

• On Bayesian A- and D-optimal experimental designs in infinite dimensions
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• Rejoinder: ``Bayesian solution uncertainty quantification for differential equations
• Error estimation and control for ODEs
• Bayesian experimental design: A review
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• Maximum Entropy Sampling and Optimal Bayesian Experimental Design
• Bayesian Probabilistic Numerical Methods
• Fourth-Order Time-Stepping for Stiff PDEs
• On Information and Sufficiency
• Numerical Methods for Ordinary Differential Equations
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