# Inference for Differential Equation Models using Relaxation via Dynamical Systems

## TL;DR

This paper introduces a fast Bayesian inference framework for ODE-based models by relaxing the ODE system with numerical methods like Runge-Kutta and Gaussian noise, enabling efficient parameter estimation.

## Contribution

It proposes a novel, computationally efficient Bayesian approach for parameter inference in ODE models using numerical relaxation and provides theoretical convergence guarantees.

## Key findings

- Method is at least 14 times faster than existing approaches
- Theoretical convergence of the posterior is established
- Explicit relations between numerical method parameters and convergence rate

## Abstract

Statistical regression models whose mean functions are represented by ordinary differential equations (ODEs) can be used to describe phenomenons dynamical in nature, which are abundant in areas such as biology, climatology and genetics. The estimation of parameters of ODE based models is essential for understanding its dynamics, but the lack of an analytical solution of the ODE makes the parameter estimation challenging. The aim of this paper is to propose a general and fast framework of statistical inference for ODE based models by relaxation of the underlying ODE system. Relaxation is achieved by a properly chosen numerical procedure, such as the Runge-Kutta, and by introducing additive Gaussian noises with small variances. Consequently, filtering methods can be applied to obtain the posterior distribution of the parameters in the Bayesian framework. The main advantage of the proposed method is computation speed. In a simulation study, the proposed method was at least 14 times faster than the other methods. Theoretical results which guarantee the convergence of the posterior of the approximated dynamical system to the posterior of true model are presented. Explicit expressions are given that relate the order and the mesh size of the Runge-Kutta procedure to the rate of convergence of the approximated posterior as a function of sample size.