Lingua: Inglese
Editore: Cambridge University Press, 2022
ISBN 10: 1107163447 ISBN 13: 9781107163447
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Lingua: Inglese
Editore: Cambridge University Press, 2022
ISBN 10: 1107163447 ISBN 13: 9781107163447
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Lingua: Inglese
Editore: Cambridge University Press, 2022
ISBN 10: 1107163447 ISBN 13: 9781107163447
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Lingua: Inglese
Editore: Cambridge University Press, 2022
ISBN 10: 1107163447 ISBN 13: 9781107163447
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Lingua: Inglese
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ISBN 10: 1107163447 ISBN 13: 9781107163447
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Lingua: Inglese
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ISBN 10: 1107163447 ISBN 13: 9781107163447
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Lingua: Inglese
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Lingua: Inglese
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ISBN 10: 1107163447 ISBN 13: 9781107163447
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Lingua: Inglese
Editore: Cambridge University Press, 2022
ISBN 10: 1107163447 ISBN 13: 9781107163447
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Lingua: Inglese
Editore: Cambridge University Press, 2022
ISBN 10: 1107163447 ISBN 13: 9781107163447
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Lingua: Inglese
Editore: Cambridge University Press, Cambridge, 2022
ISBN 10: 1107163447 ISBN 13: 9781107163447
Da: Grand Eagle Retail, Bensenville, IL, U.S.A.
Hardcover. Condizione: 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.
Lingua: Inglese
Editore: Cambridge University Press, 2022
ISBN 10: 1107163447 ISBN 13: 9781107163447
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Lingua: Inglese
Editore: Cambridge University Press 2022-06-30, 2022
ISBN 10: 1107163447 ISBN 13: 9781107163447
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ISBN 10: 1107163447 ISBN 13: 9781107163447
Da: Rarewaves USA, OSWEGO, IL, U.S.A.
Hardback. Condizione: New. 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.
Lingua: Inglese
Editore: Cambridge University Press, 2022
ISBN 10: 1107163447 ISBN 13: 9781107163447
Da: Kennys Bookshop and Art Galleries Ltd., Galway, GY, Irlanda
EUR 82,20
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Lingua: Inglese
Editore: Cambridge University Press, 2022
ISBN 10: 1107163447 ISBN 13: 9781107163447
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Lingua: Inglese
Editore: Cambridge University Press, 2022
ISBN 10: 1107163447 ISBN 13: 9781107163447
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Lingua: Inglese
Editore: Cambridge University Press, GB, 2022
ISBN 10: 1107163447 ISBN 13: 9781107163447
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Aggiungi al carrelloHardback. Condizione: New. 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.
Lingua: Inglese
Editore: Cambridge University Press, 2022
ISBN 10: 1107163447 ISBN 13: 9781107163447
Da: Kennys Bookstore, Olney, MD, U.S.A.
Condizione: New. 2022. Hardcover. . . . . . Books ship from the US and Ireland.
Lingua: Inglese
Editore: Cambridge University Press, 2022
ISBN 10: 1107163447 ISBN 13: 9781107163447
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Lingua: Inglese
Editore: Cambridge University Press, Cambridge, 2022
ISBN 10: 1107163447 ISBN 13: 9781107163447
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Aggiungi al carrelloHardcover. Condizione: 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 our UK warehouse or from our Australian or US warehouses, depending on stock availability.
Lingua: Inglese
Editore: CAMBRIDGE UNIVERSITY PRESS, 2022
ISBN 10: 1107163447 ISBN 13: 9781107163447
Da: UK BOOKS STORE, London, LONDO, Regno Unito
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Aggiungi al carrelloHardcover. Condizione: New. Brand New ! Fast Delivery "International Edition " and ship within 24-48 hours. Deliver by FedEx and Dhl, & Aramex, UPS, & USPS and we do accept APO and PO BOX Addresses. Order can be delivered worldwide within 4-6 Working days .and we do have flat rate for up to 2LB. Extra shipping charges will be requested This Item May be shipped from India, United states & United Kingdom. Depending on your location and availability.
Lingua: Inglese
Editore: Cambridge University Press, 2022
ISBN 10: 1107163447 ISBN 13: 9781107163447
Da: Revaluation Books, Exeter, Regno Unito
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Aggiungi al carrelloHardcover. Condizione: Brand New. 300 pages. 10.16x8.27x0.94 inches. In Stock.
Lingua: Inglese
Editore: Cambridge University Press, 2022
ISBN 10: 1107163447 ISBN 13: 9781107163447
Da: moluna, Greven, Germania
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Aggiungi al carrelloCondizione: New. 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.
Lingua: Inglese
Editore: Cambridge University Press, GB, 2022
ISBN 10: 1107163447 ISBN 13: 9781107163447
Da: Rarewaves USA United, OSWEGO, IL, U.S.A.
Hardback. Condizione: New. 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.
Lingua: Inglese
Editore: Cambridge University Press Jun 2022, 2022
ISBN 10: 1107163447 ISBN 13: 9781107163447
Da: AHA-BUCH GmbH, Einbeck, Germania
EUR 82,76
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Aggiungi al carrelloBuch. Condizione: Neu. Neuware - 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.
Lingua: Inglese
Editore: Cambridge University Press, Cambridge, 2022
ISBN 10: 1107163447 ISBN 13: 9781107163447
Da: AussieBookSeller, Truganina, VIC, Australia
EUR 139,79
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Aggiungi al carrelloHardcover. Condizione: 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 our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.