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A tutorial on time-evolving dynamical Bayesian inference

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A tutorial on time-evolving dynamical Bayesian inference. / Stankovski, Tomislav; Duggento, Andrea; McClintock, Peter V. E. et al.
In: European Physical Journal - Special Topics, Vol. 223, No. 13, 10.12.2014, p. 2685–2703.

Research output: Contribution to Journal/Magazine › Journal article › peer-review

Harvard

Stankovski, T, Duggento, A, McClintock, PVE & Stefanovska, A 2014, 'A tutorial on time-evolving dynamical Bayesian inference', European Physical Journal - Special Topics, vol. 223, no. 13, pp. 2685–2703. https://doi.org/10.1140/epjst/e2014-02286-7

APA

Stankovski, T., Duggento, A., McClintock, P. V. E., & Stefanovska, A. (2014). A tutorial on time-evolving dynamical Bayesian inference. European Physical Journal - Special Topics, 223(13), 2685–2703. https://doi.org/10.1140/epjst/e2014-02286-7

Vancouver

Stankovski T, Duggento A, McClintock PVE, Stefanovska A. A tutorial on time-evolving dynamical Bayesian inference. European Physical Journal - Special Topics. 2014 Dec 10;223(13):2685–2703. Epub 2014 Dec 10. doi: 10.1140/epjst/e2014-02286-7

Author

Stankovski, Tomislav ; Duggento, Andrea ; McClintock, Peter V. E. et al. / A tutorial on time-evolving dynamical Bayesian inference. In: European Physical Journal - Special Topics. 2014 ; Vol. 223, No. 13. pp. 2685–2703.

Bibtex

@article{ba72b4f2bdb04c48825326e58164d8d2,
title = "A tutorial on time-evolving dynamical Bayesian inference",
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.",
author = "Tomislav Stankovski and Andrea Duggento and McClintock, {Peter V. E.} and Aneta Stefanovska",
note = "The original publication is available at www.link.springer.com",
year = "2014",
month = dec,
day = "10",
doi = "10.1140/epjst/e2014-02286-7",
language = "English",
volume = "223",
pages = "2685–2703",
journal = "European Physical Journal - Special Topics",
issn = "1951-6355",
publisher = "EDP SCIENCES S A",
number = "13",

}

RIS

TY - JOUR

T1 - A tutorial on time-evolving dynamical Bayesian inference

AU - Stankovski, Tomislav

AU - Duggento, Andrea

AU - McClintock, Peter V. E.

AU - Stefanovska, Aneta

N1 - The original publication is available at www.link.springer.com

PY - 2014/12/10

Y1 - 2014/12/10

N2 - 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.

AB - 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.

U2 - 10.1140/epjst/e2014-02286-7

DO - 10.1140/epjst/e2014-02286-7

M3 - Journal article

VL - 223

SP - 2685

EP - 2703

JO - European Physical Journal - Special Topics

JF - European Physical Journal - Special Topics

SN - 1951-6355

IS - 13

ER -