Name
Abstract | ||
|---|---|---|
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. |
Year | Venue | Field |
|---|---|---|
2016 | 2016 IEEE 55TH CONFERENCE ON DECISION AND CONTROL (CDC) | Kernel (linear algebra),Dynamic network analysis,Spline (mathematics),Mathematical optimization,Computer science,Marginal likelihood,Network topology,Gaussian,Covariance matrix,Bayes' theorem |
DocType | ISSN | Citations |
Conference | 0743-1546 | 0 |
PageRank | References | Authors |
0.34 | 0 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Niklas Everitt | 1 | 9 | 3.27 |
| Giulio Bottegal | 2 | 82 | 13.89 |
| Cristian R. Rojas | 3 | 252 | 43.97 |
| Håkan Hjalmarsson | 4 | 1254 | 175.16 |