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A Fourier State Space Model for Bayesian ODE Filters
Gaussian ODE filtering is a probabilistic numerical method to solve ordi...
07/17/2020 ∙ by Hans Kersting, et al. ∙
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Differentiable Likelihoods for Fast Inversion of 'Likelihood-Free' Dynamical Systems
Likelihood-free (a.k.a. simulation-based) inference problems are inverse...
02/21/2020 ∙ by Hans Kersting, et al. ∙
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Probabilistic Solutions To Ordinary Differential Equations As Non-Linear Bayesian Filtering: A New Perspective
We formulate probabilistic numerical approximations to solutions of ordi...
10/08/2018 ∙ by Filip Tronarp, et al. ∙
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Convergence Rates of Gaussian ODE Filters
A recently-introduced class of probabilistic (uncertainty-aware) solvers...
07/25/2018 ∙ by Hans Kersting, et al. ∙
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Bayesian Filtering for ODEs with Bounded Derivatives
Recently there has been increasing interest in probabilistic solvers for...
09/25/2017 ∙ by Emilia Magnani, et al. ∙
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Active Uncertainty Calibration in Bayesian ODE Solvers
There is resurging interest, in statistics and machine learning, in solv...
05/11/2016 ∙ by Hans Kersting, et al. ∙
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