DagRep.11.9.102.pdf
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Probabilistic Numerical Methods - From Theory to Implementation (Dagstuhl Seminar 21432)
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Dagstuhl Reports, Volume 11, Issue 9
Volume: Dagstuhl Reports, Volume 11
Journal: Dagstuhl Reports (DagRep) - License:
Creative Commons Attribution 4.0 International license
- Publication Date: 2022-04-11
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Philipp Hennig, Ilse C.F. Ipsen, Maren Mahsereci, and Tim Sullivan. Probabilistic Numerical Methods - From Theory to Implementation (Dagstuhl Seminar 21432). In Dagstuhl Reports, Volume 11, Issue 9, pp. 102-119, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2022)
https://doi.org/10.4230/DagRep.11.9.102
https://doi.org/10.4230/DagRep.11.9.102
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.
Subject Classification
ACM Subject Classification
- Computing methodologies → Machine learning
- Mathematics of computing → Numerical analysis
Keywords
- Machine learning
- Numerical analysis
- Probabilistic numerics
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