Particle filter-based Gaussian process optimisation for parameter inference
Abstract
We propose a novel method for maximum likelihood-based parameter inference in nonlinear and/or non-Gaussian state space models. The method is an iterative procedure with three steps. At each iteration a particle filter is used to estimate the value of the log-likelihood function at the current parameter iterate. Using these log-likelihood estimates, a surrogate objective function is created by utilizing a Gaussian process model. Finally, we use a heuristic procedure to obtain a revised parameter iterate, providing an automatic trade-off between exploration and exploitation of the surrogate model. The method is profiled on two state space models with good performance both considering accuracy and computational cost.
Cite
@article{arxiv.1311.0689,
title = {Particle filter-based Gaussian process optimisation for parameter inference},
author = {Johan Dahlin and Fredrik Lindsten},
journal= {arXiv preprint arXiv:1311.0689},
year = {2016}
}
Comments
Accepted for publication in proceedings of the 19th World Congress of the International Federation of Automatic Control (IFAC), Cape Town, South Africa, August 2014. 6 pages, 4 figures