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portada Probabilistic Numerics: Computation as Machine Learning
Type
Physical Book
Author
Publisher
Language
English
Pages
410
Format
Hardcover
Dimensions
25.4 x 20.4 x 2.4 cm
Weight
1.07 kg.
ISBN13
9781107163447
Edition No.
1
Categories

Probabilistic Numerics: Computation as Machine Learning

Philipp Hennig (Author) · Michael A. Osborne (Author) · Hans P. Kersting (Author) · Cambridge University Press · Hardcover

Probabilistic Numerics: Computation as Machine Learning - Hennig, Philipp ; Osborne, Michael A. ; Kersting, Hans P.

New Book Imported to New Zealand
Delivery: 10 Jul - 16 Jul Shipping: 3 to 3 business days.
NZ$ 192.13
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NZ$ 192.13

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Synopsis "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 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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