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A tutorial on time-evolving dynamical Bayesian inference

Research output: Contribution to journalJournal article

Published
<mark>Journal publication date</mark>10/12/2014
<mark>Journal</mark>European Physical Journal - Special Topics
Issue number13
Volume223
Number of pages19
Pages (from-to)2685–2703
Publication statusPublished
Early online date10/12/14
Original languageEnglish

Abstract

In view of the current availability and variety of measured data, there is an increasing demand for powerful signal processing tools that can cope successfully with the associated problems that often arise when data are being analysed. In practice many of the data-generating systems are not only time-variable, but also influenced by neighbouring systems and subject to random fluctuations (noise) from their environments.
To encompass problems of this kind, we present a tutorial about the dynamical Bayesian inference of time-evolving coupled systems in the presence of noise. It includes the necessary theoretical description and the algorithms for its implementation. For general programming purposes, a pseudocode description is also given. Examples based on coupled phase and limit-cycle oscillators illustrate the salient features of phase dynamics inference. State domain inference is illustrated with an example of coupled chaotic oscillators. The applicability of the latter example to secure communications based on the modulation of coupling functions is outlined. MatLab codes for implementation of the method, as well as for the explicit examples, accompany the tutorial.

Bibliographic note

The original publication is available at www.link.springer.com