Probabilistic solutions to ordinary differential equations as nonlinear 
Bayesian filtering: a new perspective

Tronarp, F.; Kersting, H.; Särkkä, S.; Hennig, P.

doi:10.1007/s11222-019-09900-1

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Probabilistic solutions to ordinary differential equations as nonlinear Bayesian filtering: a new perspective

Tronarp, F., Kersting, H., Särkkä, S., & Hennig, P. (2019). Probabilistic solutions to ordinary differential equations as nonlinear Bayesian filtering: a new perspective. Statistics and Computing, 29(6), 1297-1315. doi:10.1007/s11222-019-09900-1.

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Item Permalink: https://hdl.handle.net/21.11116/0000-0006-D57C-F Version Permalink: https://hdl.handle.net/21.11116/0000-0006-D57D-E

Genre: Journal Article

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Creators:
Tronarp, F.¹, Author
Kersting, H.², Author
Särkkä, S. ¹, Author
Hennig, P.², Author

Affiliations:
1External Organizations, ou_persistent22
2Max Planck Research Group Probabilistic Numerics, Max Planck Institute for Intelligent Systems, Max Planck Society, ou_2344694

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Free keywords: Forschungsgruppe Hennig

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Language(s): eng - English

Dates: Published Online: 2019-09-18Date issued: 2019-11

Publication Status: Issued

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Rev. Type: Peer

Identifiers: DOI: 10.1007/s11222-019-09900-1
BibTex Citekey: TroKerSarHen19

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Title: Statistics and Computing

Source Genre: Journal

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Publ. Info: Norwell, MA : Kluwer Academic Publishers

Pages: - Volume / Issue: 29 (6) Sequence Number: - Start / End Page: 1297 - 1315 Identifier: ISSN: 0960-3174
CoNE: https://pure.mpg.de/cone/journals/resource/954926992159