Store besparelser
Hurtig levering
Gemte
Log ind
0
Kurv
Bøger
Bestsellers
Kommer snart
Nyheder
Spil og hobby
Hjem
Kategorier
Bestsellers
Kommer snart
Nyheder
FAQ
Konto
Kontakt
Kurv
  1. Bog
  2. Regning og matematisk analyse
  3. Numerisk analyse

Probabilistic Numerics

- Computation as Machine Learning
Af: Hans P. Kersting, Michael A. Osborne, Philipp Hennig Engelsk Hardback

Probabilistic Numerics

- Computation as Machine Learning
Af: Hans P. Kersting, Michael A. Osborne, Philipp Hennig Engelsk Hardback
Tjek vores konkurrenters priser
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.
Vis mere
Tjek vores konkurrenters priser
Normalpris
kr 573
Fragt: 39 kr
6 - 8 hverdage
20 kr
Pakkegebyr
God 4 anmeldelser på
Tjek vores konkurrenters priser
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.
Vis mere
Se mere i:
Produktdetaljer
Sprog: Engelsk
Sider: 410
ISBN-13: 9781107163447
Indbinding: Hardback
Udgave:
ISBN-10: 1107163447
Kategori: Numerisk analyse
Udg. Dato: 30 jun 2022
Længde: 25mm
Bredde: 259mm
Højde: 211mm
Forlag: Cambridge University Press
Oplagsdato: 30 jun 2022
Forfatter(e): Hans P. Kersting, Michael A. Osborne, Philipp Hennig
Forfatter(e) Hans P. Kersting, Michael A. Osborne, Philipp Hennig

Kategori Numerisk analyse

ISBN-13 9781107163447

Sprog Engelsk

Indbinding Hardback

Sider 410

Udgave

Længde 25mm

Bredde 259mm

Højde 211mm

Udg. Dato 30 jun 2022

Oplagsdato 30 jun 2022

Forlag Cambridge University Press

Kategori sammenhænge