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. N… Celý popis ↓
Porovnanie cien Probabilistic Numerics: Computation as Machine Learning Hennig Philipp
Popis 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,
Podobné produkty
Probabilistic Deep Learning
Machine Learning
Machine Learning
MACHINE LEARNING
Understanding Machine Learning
Machine Learning Algorithms
Machine Learning Refined
Graph Machine Learning
Python Machine Learning
Machine Learning Engineering
Programming Machine Learning
Introducing Machine Learning
Machine Learning with R
