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Probabilistic Numerics - Computation as Machine Learning (Hardcover)
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Probabilistic Numerics - Computation as Machine Learning (Hardcover)
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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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