A probabilistic model for the numerical solution of initial value problems

Tutkimustuotos: Lehtiartikkelivertaisarvioitu

Tutkijat

Organisaatiot

  • Max Planck Institute for Intelligent Systems

Kuvaus

We study connections between ordinary differential equation (ODE) solvers and probabilistic regression methods in statistics. We provide a new view of probabilistic ODE solvers as active inference agents operating on stochastic differential equation models that estimate the unknown initial value problem (IVP) solution from approximate observations of the solution derivative, as provided by the ODE dynamics. Adding to this picture, we show that several multistep methods of Nordsieck form can be recasted as Kalman filtering on q-times integrated Wiener processes. Doing so provides a family of IVP solvers that return a Gaussian posterior measure, rather than a point estimate. We show that some such methods have low computational overhead, nontrivial convergence order, and that the posterior has a calibrated concentration rate. Additionally, we suggest a step size adaptation algorithm which completes the proposed method to a practically useful implementation, which we experimentally evaluate using a representative set of standard codes in the DETEST benchmark set.

Yksityiskohdat

AlkuperäiskieliEnglanti
Sivut99-122
Sivumäärä24
JulkaisuSTATISTICS AND COMPUTING
Vuosikerta29
Numero1
TilaJulkaistu - 15 tammikuuta 2019
OKM-julkaisutyyppiA1 Julkaistu artikkeli, soviteltu

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