8 June 2007 Bayesian inferential framework for diagnosis of non-stationary systems
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Proceedings Volume 6602, Noise and Fluctuations in Biological, Biophysical, and Biomedical Systems; 66021A (2007) https://doi.org/10.1117/12.724697
Event: SPIE Fourth International Symposium on Fluctuations and Noise, 2007, Florence, Italy
Abstract
A Bayesian framework for parameter inference in non-stationary, nonlinear, stochastic, dynamical systems is introduced. It is applied to decode time variation of control parameters from time-series data modelling physiological signals. In this context a system of FitzHugh-Nagumo (FHN) oscillators is considered, for which synthetically generated signals are mixed via a measurement matrix. For each oscillator only one of the dynamical variables is assumed to be measured, while another variable remains hidden (unobservable). The control parameter for each FHN oscillator is varying in time. It is shown that the proposed approach allows one: (i) to reconstruct both unmeasured (hidden) variables of the FHN oscillators and the model parameters, (ii) to detect stepwise changes of control parameters for each oscillator, and (iii) to follow a continuous evolution of the control parameters in the quasi-adiabatic limit.
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Vadim N. Smelyanskiy, Dmitry G. Luchinsky, Andrea Duggento, Peter V. E. McClintock, "Bayesian inferential framework for diagnosis of non-stationary systems", Proc. SPIE 6602, Noise and Fluctuations in Biological, Biophysical, and Biomedical Systems, 66021A (8 June 2007); doi: 10.1117/12.724697; https://doi.org/10.1117/12.724697
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