Bayesian inferential framework for diagnosis of non-stationary systems

Smelyanskiy, Vadim N. and Luchinsky, Dmitry G. and Duggento, Andrea and McClintock, Peter V. E. (2007) Bayesian inferential framework for diagnosis of non-stationary systems. Proceedings of SPIE, 6602. ISSN 0277-786X

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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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Proceedings of SPIE
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Copyright 2007 Society of Photo-Optical Instrumentation Engineers. One print or electronic copy may be made for personal use only. Systematic reproduction and distribution, duplication of any material in this paper for a fee or for commercial purposes, or modification of the content of the paper are prohibited.
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18 Jun 2015 05:40
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19 Sep 2023 01:22