Bayesian Parameter Inference for Partially Observed Diffusions using Multilevel Stochastic Runge-Kutta Methods

TL;DR

This paper introduces a novel Bayesian parameter estimation method for partially observed diffusion processes using multilevel stochastic Runge-Kutta methods, improving efficiency over traditional Euler-based approaches especially in higher dimensions.

Contribution

It adapts stochastic Runge-Kutta methods for Bayesian inference in diffusion models, providing theoretical efficiency guarantees and demonstrating practical advantages in high-dimensional settings.

Findings

Achieves MSE of O(ε^2) with work complexity O(ε^{-2})

Outperforms Euler-Maruyama in high-dimensional diffusions

Validated through multiple numerical examples

Abstract

We consider the problem of Bayesian estimation of static parameters associated to a partially and discretely observed diffusion process. We assume that the exact transition dynamics of the diffusion process are unavailable, even up-to an unbiased estimator and that one must time-discretize the diffusion process. In such scenarios it has been shown how one can introduce the multilevel Monte Carlo method to reduce the cost to compute posterior expected values of the parameters for a pre-specified mean square error (MSE). These afore-mentioned methods rely on upon the Euler-Maruyama discretization scheme which is well-known in numerical analysis to have slow convergence properties. We adapt stochastic Runge-Kutta (SRK) methods for Bayesian parameter estimation of static parameters for diffusions. This can be implemented in high-dimensions of the diffusion and seemingly under-appreciated in the uncertainty quantification and statistics fields. For a class of diffusions and SRK methods, we consider the estimation of the posterior expectation of the parameters. We prove that to achieve a MSE of $\mathcal{O}(\epsilon^2)$, for $\epsilon>0$ given, the associated work is $\mathcal{O}(\epsilon^{-2})$. Whilst the latter is achievable for the Milstein scheme, this method is often not applicable for diffusions in dimension larger than two. We also illustrate our methodology in several numerical examples.