Philipp Hennig: Probabilistic Numerics | Buch

Um unseren Shop nutzen zu können, müssen Sie Javascript in Ihrem Browser aktivieren.

Cover: 9781107163447 | Probabilistic Numerics | Computation as Machine Learning | Buch | 2022

Dekorationsartikel gehören nicht zum Leistungsumfang.

Sprache: Englisch

76,15 €*

inkl. MwSt.

Versandkostenfrei per Post / DHL

Lieferzeit 1-2 Wochen

Sie können kein Produkt in den Warenkorb legen, da Javascript und technisch notwendige Cookies aktiviert sein müssen.

Kategorien:

Der Artikel ist nicht in deutscher Sprache verfasst.
Die Sprache der Produktbeschreibung und Details kann in Englisch angegeben sein. Sollten Sie Verständnisprobleme haben, wenden Sie sich gerne an uns.

Beschreibung

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.

Über den Autor

Philipp Hennig holds the Chair for the Methods of Machine Learning at the University of Tübingen, and an adjunct position at the Max Planck Institute for Intelligent Systems. He has dedicated most of his career to the development of Probabilistic Numerical Methods. Hennig's research has been supported by Emmy Noether, Max Planck and ERC fellowships. He is a co-Director of the Research Program for the Theory, Algorithms and Computations of Learning Machines at the European Laboratory for Learning and Intelligent Systems (ELLIS).

Inhaltsverzeichnis

Introduction; 1. Mathematical background; 2. Integration; 3. Linear algebra; 4. Local optimisation; 5. Global optimisation; 6. Solving ordinary differential equations; 7. The frontier; Solutions to exercises; References; Index.

Erscheinungsjahr:	2022
Fachbereich:	Analysis
Genre:	Mathematik
Rubrik:	Naturwissenschaften & Technik
Medium:	Buch
Seiten:	410
Inhalt:	Gebunden
ISBN-13:	9781107163447
ISBN-10:	1107163447
Sprache:	Englisch
Einband:	Gebunden
Autor:	Hennig, Philipp (Eberhard-Karls-Universitat Tubingen, Germany) Osborne, Michael A. (University of Oxford) Kersting, Hans P. (Ecole Normale Superieure, Paris)
Hersteller:	Cambridge University Press
Maße:	260 x 213 x 25 mm
Von/Mit:	Philipp Hennig (u. a.)
Erscheinungsdatum:	30.06.2022
Gewicht:	1,15 kg

Ähnliche Produkte