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Activity Number: 297 - SBSS Student Travel Award Session 1
Type: Topic Contributed
Date/Time: Tuesday, July 31, 2018 : 8:30 AM to 10:20 AM
Sponsor: Section on Bayesian Statistical Science
Abstract #328521 Presentation
Title: Bayesian Probabilistic Numerical Methods
Author(s): Jonathan Cockayne*
Keywords: probabilistic numerics; uncertainty quantification; gaussian processes

The emergent field of probabilistic numerics has thus far lacked rigorous statistical foundations. We establish that a class of Bayesian probabilistic numerical methods can be cast as the solution to certain non-standard Bayesian inverse problems. This allows us to establish general conditions under which Bayesian probabilistic numerical methods are well-defined, encompassing both non-linear models and non-Gaussian prior distributions. For general computation, a numerical approximation scheme is developed and its asymptotic convergence is established. The theoretical development is then extended to pipelines of numerical computation, wherein several probabilistic numerical methods are composed to perform more challenging numerical tasks. The contribution highlights an important research frontier at the interface of numerical analysis and uncertainty quantification, with some illustrative applications presented.

Authors who are presenting talks have a * after their name.

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