eBook - PDF

Probabilistic Numerics

Name: Probabilistic Numerics
ISBN: 9781316730331

Computation as Machine Learning

Philipp Hennig,

Michael A. Osborne,

Hans P. Kersting,

English
PDF
Available on iOS & Android

eBook - PDF

Probabilistic Numerics

Computation as Machine Learning

Philipp Hennig,

Michael A. Osborne,

Hans P. Kersting,

About this book

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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Yes, you can access Probabilistic Numerics by Philipp Hennig,Michael A. Osborne,Hans P. Kersting in PDF and/or ePUB format, as well as other popular books in Computer Science & Computer Science General. We have over one million books available in our catalogue for you to explore.