Non-stationary continuous time Bayesian networks are introduced. They allow the parents set of each node in a continuous time Bayesian network to change over time. Structural learning of nonstationary continuous time Bayesian networks is developed under different knowledge settings. A macroeconomic dataset is used to assess the effectiveness of learning non-stationary continuous time Bayesian networks from real-world data.

Stella, F., Villa, S. (2018). Learning Continuous Time Bayesian Networks in Non-stationary Domains (Journal Track). In Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence (pp.5656-5660). International Joint Conferences on Artificial Intelligence [10.24963/ijcai.2018/804].

Learning Continuous Time Bayesian Networks in Non-stationary Domains (Journal Track)

Stella, F
;
Villa, S
2018

Abstract

Non-stationary continuous time Bayesian networks are introduced. They allow the parents set of each node in a continuous time Bayesian network to change over time. Structural learning of nonstationary continuous time Bayesian networks is developed under different knowledge settings. A macroeconomic dataset is used to assess the effectiveness of learning non-stationary continuous time Bayesian networks from real-world data.
paper
Continuous time Bayesian networks, Artificial Intelligence, non stationary structure learning;
English
International Joint Conference on Artificial Intelligence and the 23rd European Conference on Artificial Intelligence
2018
Lang J.
Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence
9780999241127
2018
2018-
5656
5660
open
Stella, F., Villa, S. (2018). Learning Continuous Time Bayesian Networks in Non-stationary Domains (Journal Track). In Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence (pp.5656-5660). International Joint Conferences on Artificial Intelligence [10.24963/ijcai.2018/804].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10281/203837
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