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Probabilistic Numerics: Computation as Machine Learning

By (author) Philipp Hennig, By (author) Michael A. Osborne, By (author) Hans P. Kersting

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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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Normally shipped | Usually dispatched in 3 to 4 weeks as supplier is out of stock
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The Coming Wave: AI, Power and Our Future

Mustafa Suleyman
Paperback / softback
352 pages
was: R 295.95
now: R 265.95
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The Order of Time

Carlo Rovelli
Paperback / softback
224 pages
was: R 295.95
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Originally published in Italian: L'ordine del tempo (Milan: Adelphi Edizioni, 2017).

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Helgoland: The Strange and Beautiful Story of Quantum Physics

Carlo Rovelli
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208 pages
was: R 295.95
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