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.
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