Bayesian ODE solvers: the maximum a posteriori estimate

There is a growing interest in probabilistic numerical solutions to ordinary differential equations. In this paper, the maximum a posteriori estimate is studied under the class of ν times differentiable linear time-invariant Gauss–Markov priors, which can be computed with an iterated extended Kalman...

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Veröffentlicht in:	Statistics and computing Jg. 31; H. 3
Hauptverfasser:	Tronarp, Filip, Särkkä, Simo, Hennig, Philipp
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York Springer US 01.05.2021 Springer Nature B.V
Schlagworte:	Artificial Intelligence Convergence Differential equations Fields (mathematics) Hilbert space Mathematics and Statistics Nonlinear analysis Ordinary differential equations Probability and Statistics in Computer Science Smoothness Sobolev space Statistical Theory and Methods Statistics Statistics and Computing/Statistics Programs Maximum a posteriori estimation Kernel methods Probabilistic numerical methods
ISSN:	0960-3174, 1573-1375
Online-Zugang:	Volltext
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