Non-stationary continuous time Bayesian networks are introduced. They allow the parents set of each node to change over continuous time. Three settings are developed for learning non-stationary continuous time Bayesian networks from data: known transition times, known number of epochs and unknown number of epochs. A score function for each setting is derived and the corresponding learning algorithm is developed. A set of numerical experiments on synthetic data is used to compare the effectiveness of non-stationary con-Tinuous time Bayesian networks to that of non-stationary dynamic Bayesian networks. Fur-Thermore, the performance achieved by non-stationary continuous time Bayesian networks is compared to that achieved by state-of-The-Art algorithms on four real-world datasets, namely drosophila, saccharomyces cerevisiae, songbird and macroeconomics.
Stella, F., & Villa, S. (2016). Learning continuous time Bayesian networks in non-stationary domains. THE JOURNAL OF ARTIFICIAL INTELLIGENCE RESEARCH, 57, 1-37.
|Citazione:||Stella, F., & Villa, S. (2016). Learning continuous time Bayesian networks in non-stationary domains. THE JOURNAL OF ARTIFICIAL INTELLIGENCE RESEARCH, 57, 1-37.|
|Tipo:||Articolo in rivista - Articolo scientifico|
|Carattere della pubblicazione:||Scientifica|
|Presenza di un coautore afferente ad Istituzioni straniere:||Si|
|Titolo:||Learning continuous time Bayesian networks in non-stationary domains|
|Autori:||Stella, F; Villa, S|
STELLA, FABIO ANTONIO (Corresponding)
|Data di pubblicazione:||2016|
|Rivista:||THE JOURNAL OF ARTIFICIAL INTELLIGENCE RESEARCH|
|Digital Object Identifier (DOI):||http://dx.doi.org/10.1613/jair.5126|
|Appare nelle tipologie:||01 - Articolo su rivista|