Scientific field at the intersection of statistics, machine learning and applied mathematics
Probabilistic numerics is an active field of study at the intersection of applied mathematics, statistics, and machine learning centering on the concept of uncertainty in computation. In probabilistic numerics, tasks in numerical analysis such as finding numerical solutions for integration, linear algebra, optimization and simulation and differential equations are seen as problems of statistical, probabilistic, or Bayesian inference.[1][2][3][4][5]
^Hennig, P.; Osborne, M. A.; Kersting, H. P. (2022). Probabilistic Numerics(PDF). Cambridge University Press. ISBN 978-1107163447.
^Oates, C. J.; Sullivan, T. J. (2019). "A modern retrospective on probabilistic numerics". Stat. Comput. 29 (6): 1335–1351. arXiv:1901.04457. doi:10.1007/s11222-019-09902-z. S2CID 67885786.
^Owhadi, Houman; Scovel, Clint (2019). Operator-Adapted Wavelets, Fast Solvers, and Numerical Homogenization: From a Game Theoretic Approach to Numerical Approximation and Algorithm Design. Cambridge Monographs on Applied and Computational Mathematics. Cambridge: Cambridge University Press. ISBN 978-1-108-48436-7.
^Hennig, P.; Osborne, M. A.; Girolami, M. (2015). "Probabilistic numerics and uncertainty in computations". Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences. 471 (2179): 20150142, 17. arXiv:1506.01326. Bibcode:2015RSPSA.47150142H. doi:10.1098/rspa.2015.0142. PMC 4528661. PMID 26346321.
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