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Probabilistic numerical methods - from theory to implementation (Dagstuhl Seminar 21432)
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Hennig, Philipp, Ipsen, Ilse C. F., Mahsereci, Maren and Sullivan, Tim J. (2022) Probabilistic numerical methods - from theory to implementation (Dagstuhl Seminar 21432). Dagstuhl Reports, 11 (9). pp. 102-119. doi:10.4230/DagRep.11.9.102 ISSN 2192-5283.
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WRAP-Probabilistic-numerical-methods-theory-implementation-22.pdf - Published Version - Requires a PDF viewer. Available under License Creative Commons Attribution 4.0. Download (2801Kb) | Preview |
Official URL: https://drops.dagstuhl.de/opus/volltexte/2022/1592...
Abstract
Numerical methods provide the computational foundation of science, and power automated data analysis and inference in its contemporary form of machine learning. Probabilistic numerical methods aim to explicitly represent uncertainty resulting from limited computational resources and imprecise inputs in these models. With theoretical analysis well underway, software development is now a key next step to wide-spread success. This seminar brought together experts from the forefront of machine learning, statistics and numerical analysis to identify important open problems in the field and to lay the theoretical and practical foundation for a software stack for probabilistic numerical methods.
Item Type: | Journal Article | ||||
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Subjects: | Q Science > Q Science (General) Q Science > QA Mathematics |
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Divisions: | Faculty of Science, Engineering and Medicine > Engineering > Engineering Faculty of Science, Engineering and Medicine > Science > Mathematics |
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Library of Congress Subject Headings (LCSH): | Machine learning, Numerical analysis, Probabilities | ||||
Journal or Publication Title: | Dagstuhl Reports | ||||
Publisher: | Schloss Dagstuhl — Leibniz-Zentrum für Informatik | ||||
Place of Publication: | Dagstuhl, Germany | ||||
ISSN: | 2192-5283 | ||||
Official Date: | 11 April 2022 | ||||
Dates: |
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Volume: | 11 | ||||
Number: | 9 | ||||
Page Range: | pp. 102-119 | ||||
DOI: | 10.4230/DagRep.11.9.102 | ||||
Status: | Peer Reviewed | ||||
Publication Status: | Published | ||||
Access rights to Published version: | Open Access (Creative Commons) | ||||
Date of first compliant deposit: | 2 December 2022 | ||||
Date of first compliant Open Access: | 2 December 2022 | ||||
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