Probabilistic Numerics : Computation as Machine Learning
Book Details
Format
Hardback or Cased Book
ISBN-10
1107163447
ISBN-13
9781107163447
Publisher
Cambridge University Press
Imprint
Cambridge University Press
Country of Manufacture
GB
Country of Publication
GB
Publication Date
Jun 30th, 2022
Print length
410 Pages
Weight
1,146 grams
Dimensions
21.10 x 25.90 x 2.50 cms
Ksh 11,150.00
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This text provides a first comprehensive introduction to probabilistic numerics, aimed at Masters' and PhD students, as well as postgraduate researchers in artificial intelligence, computer science, statistics, and applied mathematics. It contains extensive background material, and uses figures, exercises, and worked examples to develop intuition.
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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