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Probabilistic numerics = computation...

Hennig, Philipp.

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Probabilistic numerics = computation as machine learning /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Probabilistic numerics/ Philipp Hennig, Michael A. Osborne, Hans P. Kersting.
Reminder of title:	computation as machine learning /
Author:	Hennig, Philipp.
other author:	Osborne, Michael A.
Published:	Cambridge :Cambridge University Press, : 2022.,
Description:	xii, 398 p. :ill., digital ;25 cm.
Notes:	Title from publisher's bibliographic system (viewed on 10 Jun 2022).
Subject:	Machine learning - Mathematics. -
Online resource:	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:

3442737
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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245 1 0 $a Probabilistic numerics $h [electronic resource] : $b computation as machine learning / $c Philipp Hennig, Michael A. Osborne, Hans P. Kersting.
260 $a Cambridge : $b Cambridge University Press, $c 2022.
300 $a xii, 398 p. : $b ill., digital ; $c 25 cm.
500 $a Title from publisher's bibliographic system (viewed on 10 Jun 2022).
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.
650 0 $a Machine learning $x Mathematics. $3 3442737
650 0 $a Computer algorithms. $3 523872
700 1 $a Osborne, Michael A. $3 3645654
700 1 $a Kersting, Hans P. $3 3645655
856 4 0 $u https://doi.org/10.1017/9781316681411