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...

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Probabilistic Numerics: Computation as Machine Learning

Hennig, Philipp; Osborne, Michael A.; Kersting, Hans P.

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