ML Anthology

Learning Continuous Time Bayesian Networks in Non-Stationary Domains

Simone Villa, Fabio Stella
JAIR 2016 pp. 1-37
doi:10.1613/JAIR.5126 /jair/2016/villa2016jair-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." Journal of Artificial Intelligence Research, 2016. doi:10.1613/JAIR.5126

Markdown

[Villa and Stella. "Learning Continuous Time Bayesian Networks in Non-Stationary Domains." Journal of Artificial Intelligence Research, 2016.](https://mlanthology.org/jair/2016/villa2016jair-learning/) doi:10.1613/JAIR.5126

BibTeX

@article{villa2016jair-learning,
  title     = {{Learning Continuous Time Bayesian Networks in Non-Stationary Domains}},
  author    = {Villa, Simone and Stella, Fabio},
  journal   = {Journal of Artificial Intelligence Research},
  year      = {2016},
  pages     = {1-37},
  doi       = {10.1613/JAIR.5126},
  volume    = {57},
  url       = {https://mlanthology.org/jair/2016/villa2016jair-learning/}
}