Corpus ID: 2164993

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={S. Yang and Tushar Khot and K. 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… Expand
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