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Probabilistic Numerics: Computation as Machine Learning 1st Edition
Author: Philipp Hennig,Michael A. Osborne,Hans P. Kersting(Author)
Publisher Finelybook 出版社: Cambridge University Press; 1st edition (September 30, 2022)
Language 语言: English
pages 页数: 300 pages
ISBN-10 书号: 1107163447
ISBN-13 书号: 9781107163447
Book Description
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 Author: 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.
