An empirical Bayes approach to identification of modules in dynamic networks-Reference-Cited by-同舟云学术

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An empirical Bayes approach to identification of modules in dynamic networks

Published:2018-05 Issue: Volume:91 Page:144-151
ISSN:0005-1098
Container-title:Automatica
language:en
Short-container-title:Automatica

Author:

Everitt Niklas,Bottegal Giulio,Hjalmarsson Håkan

Funder

Swedish Research Council

NewLEADS

LEARN

European Union’s Horizon 2020

Publisher

Elsevier BV

Subject

Electrical and Electronic Engineering,Control and Systems Engineering

Reference29 articles.

1. Optimal filtering;Anderson,1979

2. Julia: A fresh approach to numerical computing;Bezanson;SIAM Review,2017

3. Robust EM kernel-based methods for linear system identification;Bottegal;Automatica,2016

4. A Bayesian approach to sparse dynamic network identification;Chiuso;Automatica,2012

5. Dankers, A., & Van den Hof, P. M. J. (2015). Non-parametric identification in dynamic networks. In Proceedings of the 54th IEEE conference on decision and control (pp. 3487–3492).

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