Items related to Probabilistic Numerics: Computation as Machine Learning
- PublisherCambridge University Press
- Publication date2022
- ISBN 10 1107163447
- ISBN 13 9781107163447
- BindingHardcover
- LanguageEnglish
- Edition number1
- Number of pages410
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Probabilistic Numerics: Computation as Machine Learning
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Cambridge University Press, 2022
ISBN 10: 1107163447
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Probabilistic Numerics: Computation as Machine Learning
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Cambridge University Press, 2022
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Probabilistic Numerics
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PROBABILISTIC NUMERICS
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Probabilistic Numerics
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Probabilistic Numerics: Computation as Machine Learning
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Probabilistic Numerics
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Probabilistic Numerics
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Probabilistic Numerics: Computation as Machine Learning
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Probabilistic Numerics (Hardcover)
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Hardcover. Condition: new. Hardcover. 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. 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. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. Seller Inventory # 9781107163447
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