Please use this identifier to cite or link to this item: http://hdl.handle.net/20.500.12188/1462
Title: Dynamical Bayesian inference of time-evolving interactions: from a pair of coupled oscillators to networks of oscillators
Authors: Duggento, Andrea
Stankovski, Tomislav 
McClintock, Peter V E
Stefanovska, Aneta
Keywords: Physics - Data Analysis; Statistics and Probability
Physics - Data Analysis; Statistics and Probability
Nonlinear Sciences - Adaptation and Self-Organizing Systems
Physics - Biological Physics
Physics - Medical Physics
Issue Date: Dec-2012
Journal: Physical review. E, Statistical, nonlinear, and soft matter physics
Abstract: Living systems have time-evolving interactions that, until recently, could not be identified accurately from recorded time series in the presence of noise. Stankovski et al. [Phys. Rev. Lett. 109, 024101 (2012)] introduced a method based on dynamical Bayesian inference that facilitates the simultaneous detection of time-varying synchronization, directionality of influence, and coupling functions. It can distinguish unsynchronized dynamics from noise-induced phase slips. The method is based on phase dynamics, with Bayesian inference of the time-evolving parameters being achieved by shaping the prior densities to incorporate knowledge of previous samples. We now present the method in detail using numerically generated data, data from an analog electronic circuit, and cardiorespiratory data. We also generalize the method to encompass networks of interacting oscillators and thus demonstrate its applicability to small-scale networks.
URI: http://hdl.handle.net/20.500.12188/1462
DOI: 10.1103/PhysRevE.86.061126
Appears in Collections:Faculty of Medicine: Journal Articles

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