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 [10.1613/jair.5126].

Learning continuous time Bayesian networks in non-stationary domains

STELLA, FABIO ANTONIO
;
2016

Abstract

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.
Articolo in rivista - Articolo scientifico
Continuous time Bayesian networks; non stationary learning; biology, finance
English
2016
57
1
37
partially_open
Stella, F., Villa, S. (2016). Learning continuous time Bayesian networks in non-stationary domains. THE JOURNAL OF ARTIFICIAL INTELLIGENCE RESEARCH, 57, 1-37 [10.1613/jair.5126].
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