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Lookup NU author(s): Professor Chris Oates
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© 2023, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature. We analyse the calibration of BayesCG under the Krylov prior. BayesCG is a probabilistic numeric extension of the Conjugate Gradient (CG) method for solving systems of linear equations with real symmetric positive definite coefficient matrix. In addition to the CG solution, BayesCG also returns a posterior distribution over the solution. In this context, a posterior distribution is said to be ‘calibrated’ if the CG error is well-described, in a precise distributional sense, by the posterior spread. Since it is known that BayesCG is not calibrated, we introduce two related weaker notions of calibration, whose departures from exact calibration can be quantified. Numerical experiments confirm that, under low-rank approximate Krylov posteriors, BayesCG is only slightly optimistic and exhibits the characteristics of a calibrated solver, and is computationally competitive with CG.
Author(s): Reid TW, Ipsen ICF, Cockayne J, Oates CJ
Publication type: Article
Publication status: Published
Journal: Numerische Mathematik
Year: 2023
Volume: 155
Pages: 239-288
Print publication date: 01/12/2023
Online publication date: 12/10/2023
Acceptance date: 14/09/2023
ISSN (print): 0029-599X
ISSN (electronic): 0945-3245
Publisher: Springer Nature
URL: https://doi.org/10.1007/s00211-023-01375-7
DOI: 10.1007/s00211-023-01375-7
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