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Learning Continuous Time Bayesian Networks in Non-Stationary Domains

Simone Villa, Fabio Stella
IJCAI 2018 pp. 5656-5660
doi:10.24963/IJCAI.2018/804 /ijcai/2018/villa2018ijcai-learning/

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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Text

Villa and Stella. "Learning Continuous Time Bayesian Networks in Non-Stationary Domains." International Joint Conference on Artificial Intelligence, 2018. doi:10.24963/IJCAI.2018/804

Markdown

[Villa and Stella. "Learning Continuous Time Bayesian Networks in Non-Stationary Domains." International Joint Conference on Artificial Intelligence, 2018.](https://mlanthology.org/ijcai/2018/villa2018ijcai-learning/) doi:10.24963/IJCAI.2018/804

BibTeX

@inproceedings{villa2018ijcai-learning,
  title     = {{Learning Continuous Time Bayesian Networks in Non-Stationary Domains}},
  author    = {Villa, Simone and Stella, Fabio},
  booktitle = {International Joint Conference on Artificial Intelligence},
  year      = {2018},
  pages     = {5656-5660},
  doi       = {10.24963/IJCAI.2018/804},
  url       = {https://mlanthology.org/ijcai/2018/villa2018ijcai-learning/}
}