License: Creative Commons Attribution 4.0 International license (CC BY 4.0)
When quoting this document, please refer to the following
DOI: 10.4230/DagRep.11.9.102
URN: urn:nbn:de:0030-drops-159208
URL: https://drops.dagstuhl.de/opus/volltexte/2022/15920/
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Hennig, Philipp ; Ipsen, Ilse C.F. ; Mahsereci, Maren ; Sullivan, Tim
Weitere Beteiligte (Hrsg. etc.): Philipp Hennig and Ilse C.F. Ipsen and Maren Mahsereci and Tim Sullivan

Probabilistic Numerical Methods - From Theory to Implementation (Dagstuhl Seminar 21432)

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dagrep_v011_i009_p102_21432.pdf (3 MB)


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.

BibTeX - Entry

@Article{hennig_et_al:DagRep.11.9.102,
  author =	{Hennig, Philipp and Ipsen, Ilse C.F. and Mahsereci, Maren and Sullivan, Tim},
  title =	{{Probabilistic Numerical Methods - From Theory to Implementation (Dagstuhl Seminar 21432)}},
  pages =	{102--119},
  journal =	{Dagstuhl Reports},
  ISSN =	{2192-5283},
  year =	{2022},
  volume =	{11},
  number =	{9},
  editor =	{Hennig, Philipp and Ipsen, Ilse C.F. and Mahsereci, Maren and Sullivan, Tim},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/opus/volltexte/2022/15920},
  URN =		{urn:nbn:de:0030-drops-159208},
  doi =		{10.4230/DagRep.11.9.102},
  annote =	{Keywords: Machine learning, Numerical analysis, Probabilistic numerics}
}

Keywords: Machine learning, Numerical analysis, Probabilistic numerics
Collection: DagRep, Volume 11, Issue 9
Issue Date: 2022
Date of publication: 11.04.2022


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