KEYWORDS: Oscillators, Systems modeling, Solids, Stochastic processes, Data modeling, Signal generators, Neurons, Control systems, Diagnostics, Diffusion
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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