Priors for Linear Differential Equations
| dc.contributor.author | Lange-Hegermann, Markus | |
| dc.contributor.editor | David, Klaus | |
| dc.contributor.editor | Geihs, Kurt | |
| dc.contributor.editor | Lange, Martin | |
| dc.contributor.editor | Stumme, Gerd | |
| dc.date.accessioned | 2019-08-27T12:55:24Z | |
| dc.date.available | 2019-08-27T12:55:24Z | |
| dc.date.issued | 2019 | |
| dc.description.abstract | We algorithmically construct multi-output Gaussian process priors which satisfy linear differential equations. We parametrize all solutions of the differential equations using Gröbner bases for controllable systems. If successful, a push forward along the parametrization is the desired prior. This prior yields an interpretable machine learning model, which can combine linear differential equations with noisy data points. | en |
| dc.identifier.doi | 10.18420/inf2019_38 | |
| dc.identifier.isbn | 978-3-88579-688-6 | |
| dc.identifier.pissn | 1617-5468 | |
| dc.identifier.uri | https://dl.gi.de/handle/20.500.12116/24986 | |
| dc.language.iso | en | |
| dc.publisher | Gesellschaft für Informatik e.V. | |
| dc.relation.ispartof | INFORMATIK 2019: 50 Jahre Gesellschaft für Informatik – Informatik für Gesellschaft | |
| dc.relation.ispartofseries | Lecture Notes in Informatics (LNI) - Proceedings, Volume P-294 | |
| dc.subject | Gaussian process | |
| dc.subject | regression | |
| dc.subject | differential equation | |
| dc.subject | kernel | |
| dc.subject | Gröbner basis | |
| dc.title | Priors for Linear Differential Equations | en |
| dc.type | Text/Conference Paper | |
| gi.citation.endPage | 270 | |
| gi.citation.publisherPlace | Bonn | |
| gi.citation.startPage | 269 | |
| gi.conference.date | 23.-26. September 2019 | |
| gi.conference.location | Kassel | |
| gi.conference.sessiontitle | Data Science |
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