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Probabilistic numerics = computation as machine learning /

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Probabilistic numerics/ Philipp Hennig, Michael A. Osborne, Hans P. Kersting.
其他題名:	computation as machine learning /
作者:	Hennig, Philipp.
其他作者:	Osborne, Michael A.
出版者:	Cambridge :Cambridge University Press, : 2022.,
面頁冊數:	xii, 398 p. :ill., digital ; : 25 cm.;
附註:	Title from publisher's bibliographic system (viewed on 10 Jun 2022).
標題:	Machine learning - Mathematics. -
電子資源:	https://doi.org/10.1017/9781316681411
ISBN:	9781316681411

Probabilistic numerics = computation as machine learning /
Hennig, Philipp.

Probabilistic numericscomputation as machine learning /[electronic resource] :Philipp Hennig, Michael A. Osborne, Hans P. Kersting. - Cambridge :Cambridge University Press,2022. - xii, 398 p. :ill., digital ;25 cm.

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.

ISBN: 9781316681411Subjects--Topical Terms:

1340126
Machine learning
--Mathematics.

LC Class. No.: Q325.5 / .H46 2022

Dewey Class. No.: 006.31

Probabilistic numerics = computation as machine learning /
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520 $a 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.
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