Probabilistic Numerics: Computation as Machine Learning Hennig Philipp
Probabilistic Numerics: Computation as Machine Learning Hennig Philipp Probabilistic numerical computation formalises the connection between machine learning and applied mathematics. They estimate…
Specifikacia Probabilistic Numerics: Computation as Machine Learning Hennig Philipp
Probabilistic Numerics: Computation as Machine Learning Hennig Philipp
Probabilistic numerical computation formalises the connection between machine learning and applied mathematics. 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. Numerical algorithms approximate intractable quantities from computable ones.
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. In other words, they infer a latent quantity from data. 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,
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