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Proceedings Paper

Bayesian inferential framework for diagnostic of non-stationary systems
Author(s): Vadim N. Smelyanskiy; Dmitry G. Luchinsky; Andrea Duggento; Peter V. E. McClintock
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Paper 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.

Paper Details

Date Published: 8 June 2007
PDF: 12 pages
Proc. SPIE 6602, Noise and Fluctuations in Biological, Biophysical, and Biomedical Systems, 66021A (8 June 2007); doi: 10.1117/12.724697
Show Author Affiliations
Vadim N. Smelyanskiy, NASA Ames Research Ctr. (United States)
Dmitry G. Luchinsky, NASA Ames Research Ctr. (United States)
Andrea Duggento, Lancaster Univ. (United Kingdom)
Peter V. E. McClintock, Lancaster Univ. (United Kingdom)

Published in SPIE Proceedings Vol. 6602:
Noise and Fluctuations in Biological, Biophysical, and Biomedical Systems
Sergey M. Bezrukov, Editor(s)

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