Graphical Continuous Lyapunov Models
Abstract
The linear Lyapunov equation of a covariance matrix parametrizes theequilibrium covariance matrix of a stochastic process. This parametrization canbe interpreted as a new graphical model class, and we show how the model classbehaves under marginalization and introduce a method for structure learning via$\ell_1$-penalized loss minimization. Our proposed method is demonstrated tooutperform alternative structure learning algorithms in a simulation study, andwe illustrate its application for protein phosphorylation network reconstruction.
Cite
Text
Varando and Richard Hansen. "Graphical Continuous Lyapunov Models." Uncertainty in Artificial Intelligence, 2020.Markdown
[Varando and Richard Hansen. "Graphical Continuous Lyapunov Models." Uncertainty in Artificial Intelligence, 2020.](https://mlanthology.org/uai/2020/varando2020uai-graphical/)BibTeX
@inproceedings{varando2020uai-graphical,
title = {{Graphical Continuous Lyapunov Models}},
author = {Varando, Gherardo and Richard Hansen, Niels},
booktitle = {Uncertainty in Artificial Intelligence},
year = {2020},
pages = {989-998},
volume = {124},
url = {https://mlanthology.org/uai/2020/varando2020uai-graphical/}
}