Abstract
A new approach was recently introduced for the task of estimation of parameters of chaotic dynamical systems. Here we apply the method for stochastic differential equation (SDE) systems. It turns out that the basic version of the approach does not identify such systems. However, a modification is presented that enables efficient parameter estimation of SDE models. We test the approach with basic SDE examples, compare the results to those obtained by usual state-space filtering methods, and apply it to more complex cases where the more traditional methods are no more available.
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This work was supported by the Centre of Excellence of Inverse Problems, Academy of Finland.
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Haario, H., Hakkarainen, J., Maraia, R., Springer, S. (2019). Correlation Integral Likelihood for Stochastic Differential Equations. In: de Gier, J., Praeger, C., Tao, T. (eds) 2017 MATRIX Annals. MATRIX Book Series, vol 2. Springer, Cham. https://doi.org/10.1007/978-3-030-04161-8_3
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DOI: https://doi.org/10.1007/978-3-030-04161-8_3
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