Black Box Probabilistic Numerics

Abstract

Probabilistic numerics casts numerical tasks, such the numerical solution of differential equations, as inference problems to be solved. One approach is to model the unknown quantity of interest as a random variable, and to constrain this variable using data generated during the course of a traditional numerical method. However, data may be nonlinearly related to the quantity of interest, rendering the proper conditioning of random variables difficult and limiting the range of numerical tasks that can be addressed. Instead, this paper proposes to construct probabilistic numerical methods based only on the final output from a traditional method. A convergent sequence of approximations to the quantity of interest constitute a dataset, from which the limiting quantity of interest can be extrapolated, in a probabilistic analogue of Richardson's deferred approach to the limit. This black box approach (1) massively expands the range of tasks to which probabilistic numerics can be applied, (2) inherits the features and performance of state-of-the-art numerical methods, and (3) enables provably higher orders of convergence to be achieved. Applications are presented for nonlinear ordinary and partial differential equations, as well as for eigenvalue problems-a setting for which no probabilistic numerical methods have yet been developed.

An importance sampling approach for reliable and efficient inference in Bayesian ordinary differential equation models

“…Probabilistic numerical methods (Hennig et al, 2015) view the numerical problems probabilistically and can give uncertainty estimates for the solution. Teymur et al (2021) used Gaussian process regression to estimate a distribution for the exact solution given a series of approximations of different accuracy. These methods are designed for performing a fixed numerical problem probabilistically, and it is not clear how to use them in Bayesian inference of models that contain numerical problems with unknown parameters.…”