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• W2084638931 abstract "The problem of estimating unobserved states of spatially extended dynamical systems poses an inverse problem, which can be solved approximately by a recently developed variant of Kalman filtering; in order to provide the model of the dynamics with more flexibility with respect to space and time, we suggest to combine the concept of GARCH modelling of covariance, well known in econometrics, with Kalman filtering. We formulate this algorithm for spatiotemporal systems governed by stochastic diffusion equations and demonstrate its feasibility by presenting a numerical simulation designed to imitate the situation of the generation of electroencephalographic recordings by the human cortex." @default.
• W2084638931 created "2016-06-24" @default.
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• W2084638931 date "2004-12-01" @default.
• W2084638931 modified "2023-09-24" @default.
• W2084638931 title "GARCH modelling of covariance in dynamical estimation of inverse solutions" @default.
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• W2084638931 doi "https://doi.org/10.1016/j.physleta.2004.10.045" @default.
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