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Giurghita, Diana; Husmeier, Dirk (2016)
Languages: English
Types: Other
Subjects:
An increasing number of scientific disciplines, most notably the life sciences and\ud health care, have become more quantitative, describing complex systems with coupled nonlinear\ud di↵erential equations. While powerful algorithms for numerical simulations from these systems\ud have been developed, statistical inference of the system parameters is still a challenging problem.\ud A promising approach is based on the unscented Kalman filter (UKF), which has seen\ud a variety of recent applications, from soft tissue mechanics to chemical kinetics. The present\ud study investigates the dependence of the accuracy of parameter estimation on the initialisation.\ud Based on three toy systems that capture typical features of real-world complex systems: limit\ud cycles, chaotic attractors and intrinsic stochasticity, we carry out repeated simulations on a large\ud range of independent data instantiations. Our study allows a quantification of the accuracy of\ud inference, measured in terms of two alternative distance measures in function and parameter\ud space, in dependence on the initial deviation from the ground truth.

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