Probabilistic Numerics
Computation as Machine Learning

A thorough introduction to probabilistic numerics showing how to build more flexible, efficient, or customised algorithms for computation.

Philipp Hennig (Author), Michael A. Osborne (Author), Hans P. Kersting (Author)

9781107163447, Cambridge University Press

Hardback, published 30 June 2022

410 pages
26 x 20.8 x 2.3 cm, 1.16 kg

'… the machine learning background of the authors comes through clearly in the book … I thoroughly recommend it.' Chris J. Oates, SIAM Review

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.

Introduction
1. Mathematical background
2. Integration
3. Linear algebra
4. Local optimisation
5. Global optimisation
6. Solving ordinary differential equations
7. The frontier
Solutions to exercises
References
Index.

Subject Areas: Machine learning [UYQM], Mathematical theory of computation [UYA], Algorithms & data structures [UMB], Probability & statistics [PBT], Numerical analysis [PBKS]