中图分类法:	TP181 版次：
著者:	Hennig, Philipp,
题名:	Probabilistic numerics : [ computation as machine learning /] / ,
出版发行:	出版地： Cambridge : 出版社： Cambridge University Press, 出版日期： 2022.
载体形态:	xii, 398 pages ; 25 cm
内容提要:	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.
主题词:	Machine learning Mathematics.
主题词:	Computer algorithms.
主要责任者:	Osborne, Michael A., Osborne, Michael A.,
主要责任者:	Kersting, Hans P., Kersting, Hans P.,