Probabilistic numerics: computation as machine learning
Probabilistic numerical computation formalises the connection between machine learning and applied mathematics. Numerical algorithms approximate intractable quantities from computable ones. They estimate integrals from evaluations of the integrand, or the path of a dynamical system described by diff...
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| Hauptverfasser: | , , |
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| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Cambridge
Cambridge University Press
2022
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| Schlagworte: | |
| Online-Zugang: | BSB01 BTU01 FHN01 UBG01 UBM01 URL des Erstveröffentlichers |
| Zusammenfassung: | Probabilistic numerical computation formalises the connection between machine learning and applied mathematics. Numerical algorithms approximate intractable quantities from computable ones. They estimate integrals from evaluations of the integrand, or the path of a dynamical system described by differential equations from evaluations of the vector field. In other words, they infer a latent quantity from data. This book shows that it is thus formally possible to think of computational routines as learning machines, and to use the notion of Bayesian inference to build more flexible, efficient, or customised algorithms for computation. The text caters for Masters' and PhD students, as well as postgraduate researchers in artificial intelligence, computer science, statistics, and applied mathematics. Extensive background material is provided along with a wealth of figures, worked examples, and exercises (with solutions) to develop intuition |
| Beschreibung: | Title from publisher's bibliographic system (viewed on 10 Jun 2022) |
| Beschreibung: | 1 Online-Ressource (xii, 398 Seiten) |
| ISBN: | 9781316681411 |
| DOI: | 10.1017/9781316681411 |
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Datensatz im Suchindex
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| author | Hennig, Philipp Osborne, Michael A. Kersting, Hans 1990- |
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| author_facet | Hennig, Philipp Osborne, Michael A. Kersting, Hans 1990- |
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| dewey-ones | 006 - Special computer methods |
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| discipline | Informatik Mathematik |
| discipline_str_mv | Informatik Mathematik |
| doi_str_mv | 10.1017/9781316681411 |
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| illustrated | Not Illustrated |
| index_date | 2024-07-03T20:18:15Z |
| indexdate | 2024-07-10T09:36:30Z |
| institution | BVB |
| isbn | 9781316681411 |
| language | English |
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| physical | 1 Online-Ressource (xii, 398 Seiten) |
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| publisher | Cambridge University Press |
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| spelling | Hennig, Philipp (DE-588)1178518078 aut Probabilistic numerics computation as machine learning Philipp Hennig, Michael A. Osborne, Hans P. Kersting Cambridge Cambridge University Press 2022 1 Online-Ressource (xii, 398 Seiten) txt rdacontent c rdamedia cr rdacarrier Title from publisher's bibliographic system (viewed on 10 Jun 2022) Probabilistic numerical computation formalises the connection between machine learning and applied mathematics. Numerical algorithms approximate intractable quantities from computable ones. They estimate integrals from evaluations of the integrand, or the path of a dynamical system described by differential equations from evaluations of the vector field. In other words, they infer a latent quantity from data. This book shows that it is thus formally possible to think of computational routines as learning machines, and to use the notion of Bayesian inference to build more flexible, efficient, or customised algorithms for computation. The text caters for Masters' and PhD students, as well as postgraduate researchers in artificial intelligence, computer science, statistics, and applied mathematics. Extensive background material is provided along with a wealth of figures, worked examples, and exercises (with solutions) to develop intuition Machine learning / Mathematics Computer algorithms Numerische Mathematik (DE-588)4042805-9 gnd rswk-swf Maschinelles Lernen (DE-588)4193754-5 gnd rswk-swf Wahrscheinlichkeit (DE-588)4137007-7 gnd rswk-swf Numerische Mathematik (DE-588)4042805-9 s Maschinelles Lernen (DE-588)4193754-5 s Wahrscheinlichkeit (DE-588)4137007-7 s DE-604 Osborne, Michael A. (DE-588)1263583865 aut Kersting, Hans 1990- (DE-588)1229213430 aut Erscheint auch als Druck-Ausgabe 978-1-107-16344-7 https://doi.org/10.1017/9781316681411 Verlag URL des Erstveröffentlichers Volltext |
| spellingShingle | Hennig, Philipp Osborne, Michael A. Kersting, Hans 1990- Probabilistic numerics computation as machine learning Machine learning / Mathematics Computer algorithms Numerische Mathematik (DE-588)4042805-9 gnd Maschinelles Lernen (DE-588)4193754-5 gnd Wahrscheinlichkeit (DE-588)4137007-7 gnd |
| subject_GND | (DE-588)4042805-9 (DE-588)4193754-5 (DE-588)4137007-7 |
| title | Probabilistic numerics computation as machine learning |
| title_auth | Probabilistic numerics computation as machine learning |
| title_exact_search | Probabilistic numerics computation as machine learning |
| title_exact_search_txtP | Probabilistic numerics computation as machine learning |
| title_full | Probabilistic numerics computation as machine learning Philipp Hennig, Michael A. Osborne, Hans P. Kersting |
| title_fullStr | Probabilistic numerics computation as machine learning Philipp Hennig, Michael A. Osborne, Hans P. Kersting |
| title_full_unstemmed | Probabilistic numerics computation as machine learning Philipp Hennig, Michael A. Osborne, Hans P. Kersting |
| title_short | Probabilistic numerics |
| title_sort | probabilistic numerics computation as machine learning |
| title_sub | computation as machine learning |
| topic | Machine learning / Mathematics Computer algorithms Numerische Mathematik (DE-588)4042805-9 gnd Maschinelles Lernen (DE-588)4193754-5 gnd Wahrscheinlichkeit (DE-588)4137007-7 gnd |
| topic_facet | Machine learning / Mathematics Computer algorithms Numerische Mathematik Maschinelles Lernen Wahrscheinlichkeit |
| url | https://doi.org/10.1017/9781316681411 |
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