Sequential discretisation schemes for a class of stochastic differential equations and their application to Bayesian filtering

TL;DR

This paper presents a new predictor-corrector discretisation scheme for stochastic differential equations that is suitable for high-dimensional models, offering improved efficiency and accuracy in Bayesian filtering applications.

Contribution

The paper introduces a novel sequential predictor-corrector scheme with weak order 1.0 convergence, optimized for high-dimensional stochastic systems and Bayesian filtering.

Findings

The new scheme allows larger time steps than Euler-Maruyama.

It reduces computational cost for high-dimensional models.

Ensemble Kalman filters with the new scheme perform better with noisier data.

Abstract

We introduce a predictor-corrector discretisation scheme for the numerical integration of a class of stochastic differential equations and prove that it converges with weak order 1.0. The key feature of the new scheme is that it builds up sequentially (and recursively) in the dimension of the state space of the solution, hence making it suitable for approximations of high-dimensional state space models. We show, using the stochastic Lorenz 96 system as a test model, that the proposed method can operate with larger time steps than the standard Euler-Maruyama scheme and, therefore, generate valid approximations with a smaller computational cost. We also introduce the theoretical analysis of the error incurred by the new predictor-corrector scheme when used as a building block for discrete-time Bayesian filters for continuous-time systems. Finally, we assess the performance of several ensemble Kalman filters that incorporate the proposed sequential predictor-corrector Euler scheme and the standard Euler-Maruyama method. The numerical experiments show that the filters employing the new sequential scheme can operate with larger time steps, smaller Monte Carlo ensembles and noisier systems.