Corpus ID: 52198431

Cluster Variational Approximations for Structure Learning of Continuous-Time Bayesian Networks from Incomplete Data

@inproceedings{Linzner2018ClusterVA,
  title={Cluster Variational Approximations for Structure Learning of Continuous-Time Bayesian Networks from Incomplete Data},
  author={Dominik Linzner and H. Koeppl},
  booktitle={NeurIPS},
  year={2018}
}
Continuous-time Bayesian networks (CTBNs) constitute a general and powerful framework for modeling continuous-time stochastic processes on networks. This makes them particularly attractive for learning the directed structures among interacting entities. However, if the available data is incomplete, one needs to simulate the prohibitively complex CTBN dynamics. Existing approximation techniques, such as sampling and low-order variational methods, either scale unfavorably in system size, or are… Expand
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