Variational State and Parameter Estimation
Machine Learning
2020-12-15 v1 Machine Learning
Abstract
This paper considers the problem of computing Bayesian estimates of both states and model parameters for nonlinear state-space models. Generally, this problem does not have a tractable solution and approximations must be utilised. In this work, a variational approach is used to provide an assumed density which approximates the desired, intractable, distribution. The approach is deterministic and results in an optimisation problem of a standard form. Due to the parametrisation of the assumed density selected first- and second-order derivatives are readily available which allows for efficient solutions. The proposed method is compared against state-of-the-art Hamiltonian Monte Carlo in two numerical examples.
Cite
@article{arxiv.2012.07269,
title = {Variational State and Parameter Estimation},
author = {Jarrad Courts and Johannes Hendriks and Adrian Wills and Thomas Schön and Brett Ninness},
journal= {arXiv preprint arXiv:2012.07269},
year = {2020}
}