Identification of modules in dynamic networks: an empirical Bayes approach

N. Everitt, G. Bottegal, C.R. Rojas, H. Hjalmarsson

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

6 Citations (Scopus)
1 Downloads (Pure)


We address the problem of identifying a specific module in a dynamic network, assuming known topology. We express the dynamics by an acyclic network composed of two blocks where the first block accounts for the relation between the known reference signals and the input to the target module, while the second block contains the target module. Using an empirical Bayes approach, we model the first block as a Gaussian vector with covariance matrix (kernel) given by the recently introduced stable spline kernel. The parameters of the target module are estimated by solving a marginal likelihood problem with a novel iterative scheme based on the Expectation-Maximization algorithm. Numerical experiments illustrate the effectiveness of the proposed method.
Original languageEnglish
Title of host publication2016 IEEE 55th Conference on Decision and Control (CDC) , ARIA Resort & Casino, December 12-14, 2016, Las Vegas, USA
Place of PublicationPiscataway
PublisherInstitute of Electrical and Electronics Engineers
ISBN (Electronic)978-1-5090-1837-6
Publication statusPublished - 2016
Externally publishedYes
Event55th IEEE Conference on Decision and Control (CDC 2016) - Aria Resort and Casino, Las Vegas, United States
Duration: 12 Dec 201614 Dec 2016
Conference number: 55


Conference55th IEEE Conference on Decision and Control (CDC 2016)
Abbreviated titleCDC02016
CountryUnited States
CityLas Vegas
Internet address

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