A Bayesian Approach to Sparse plus Low rank Network Identification
Optimization and Control
2015-09-29 v2 Machine Learning
Abstract
We consider the problem of modeling multivariate time series with parsimonious dynamical models which can be represented as sparse dynamic Bayesian networks with few latent nodes. This structure translates into a sparse plus low rank model. In this paper, we propose a Gaussian regression approach to identify such a model.
Cite
@article{arxiv.1503.07340,
title = {A Bayesian Approach to Sparse plus Low rank Network Identification},
author = {Mattia Zorzi and Alessandro Chiuso},
journal= {arXiv preprint arXiv:1503.07340},
year = {2015}
}