Vectorized and Parallel Particle Filter SMC Parameter Estimation for Stiff ODEs

TL;DR

This paper introduces a vectorized and parallelizable particle filter SMC method tailored for parameter estimation in stiff ODE systems, significantly improving computational efficiency in complex, time-dependent models.

Contribution

The paper presents a reformulation of particle filter SMC algorithms that enhances parallel and vectorized implementation, addressing computational challenges in estimating parameters of stiff ODEs.

Findings

Achieved significant speedups with parallel and vectorized implementations.

Demonstrated effectiveness on MATLAB examples with stiff ODEs.

Improved computational feasibility for large-scale parameter estimation.

Abstract

Particle filter (PF) sequential Monte Carlo (SMC) methods are very attractive for the estimation of parameters of time dependent systems where the data is either not all available at once, or the range of time constants is wide enough to create problems in the numerical time propagation of the states. The need to evolve a large number of particles makes PF-based methods computationally challenging, the main bottlenecks being the time propagation of each particle and the large number of particles. While parallelization is typically advocated to speed up the computing time, vectorization of the algorithm on a single processor may result in even larger speedups for certain problems. In this paper we present a formulation of the PF-SMC class of algorithms proposed in Arnold et al. (2013), which is particularly amenable to a parallel or vectorized computing environment, and we illustrate the performance with a few computed examples in MATLAB.