• Computer Science
  • Published in AAAI 2016

Learning Continuous-Time Bayesian Networks in Relational Domains: A Non-Parametric Approach

@inproceedings{Yang2016LearningCB,
  title={Learning Continuous-Time Bayesian Networks in Relational Domains: A Non-Parametric Approach},
  author={Shuo Yang and Tushar Khot and Kristian Kersting and Sriraam Natarajan},
  booktitle={AAAI},
  year={2016}
}
Many real world applications in medicine, biology, communication networks, web mining, and economics, among others, involve modeling and learning structured stochastic processes that evolve over continuous time. Existing approaches, however, have focused on propositional domains only. Without extensive feature engineering, it is difficult— if not impossible—to apply them within relational domains where we may have varying number of objects and relations among them. We therefore develop the… CONTINUE READING

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