Joint Bayesian Inference of Parameter and Discretization Error Uncertainties in ODE Models

Abstract

We address the problem of Bayesian inference for parameters in ordinary differential equation (ODE) models based on observational data. Conventional approaches in this setting typically rely on numerical solvers such as the Euler or Runge-Kutta methods. However, these methods generally do not account for the discretization error induced by discretizing the ODE model. We propose a Bayesian inference framework for ODE models that explicitly quantifies discretization errors. Our method models discretization error as a random variable and performs Bayesian inference on both ODE parameters and variances of the randomized discretization errors, referred to as the discretization error variance. A key idea of our approach is the introduction of a Markov prior on the temporal evolution of the discretization error variances, enabling the inference problem to be formulated as a state-space model. Furthermore, we propose a specific form of the Markov prior that arises naturally from standard discretization error analysis. This prior depends on the step size in the numerical solver, and we discuss its asymptotic property in the limit as the step size approaches zero. Numerical experiments illustrate that the proposed method can simultaneously quantify uncertainties in both the ODE parameters and the discretization errors, and can produce posterior distributions over the parameters with broader support by accounting for discretization error.