Inference in Nonlinear Systems with Unscented Kalman Filters

Giurghita, D. and Husmeier, D. (2016) Inference in Nonlinear Systems with Unscented Kalman Filters. In: 22nd International Conference on Computational Statistics (COMPSTAT 2016), Oviedo, Spain, 23-26 Aug 2016, pp. 383-393. ISBN 9789073592360

121162.pdf - Accepted Version



An increasing number of scientific disciplines, most notably the life sciences and health care, have become more quantitative, describing complex systems with coupled nonlinear di↵erential equations. While powerful algorithms for numerical simulations from these systems have been developed, statistical inference of the system parameters is still a challenging problem. A promising approach is based on the unscented Kalman filter (UKF), which has seen a variety of recent applications, from soft tissue mechanics to chemical kinetics. The present study investigates the dependence of the accuracy of parameter estimation on the initialisation. Based on three toy systems that capture typical features of real-world complex systems: limit cycles, chaotic attractors and intrinsic stochasticity, we carry out repeated simulations on a large range of independent data instantiations. Our study allows a quantification of the accuracy of inference, measured in terms of two alternative distance measures in function and parameter space, in dependence on the initial deviation from the ground truth.

Item Type:Conference Proceedings
Glasgow Author(s) Enlighten ID:Husmeier, Professor Dirk
Authors: Giurghita, D., and Husmeier, D.
College/School:College of Science and Engineering > School of Mathematics and Statistics > Statistics
Copyright Holders:Copyright © 2016 The International Statistical Institute/International Association for Statistical Computing
First Published:First published in 22nd International Conference on Computational Statistics (COMPSTAT 2016): 383-393
Publisher Policy:Reproduced with the permission of the Publisher
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