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Scalable Structure Learning of Continuous-Time Bayesian Networks from Incomplete Data

Linzner, Dominik and Schmidt, Michael and Koeppl, Heinz (2019):
Scalable Structure Learning of Continuous-Time Bayesian Networks from Incomplete Data.
In: Proceedings of Neural Information Processing Systems, In: 33rd Conference on Neural Information Processing Systems (NeurIPS 2019), Vancouver, Canada, 09.12.-13.12.2019, [Online-Edition: https://arxiv.org/abs/1909.04570],
[Conference or Workshop Item]

Item Type: Conference or Workshop Item
Erschienen: 2019
Creators: Linzner, Dominik and Schmidt, Michael and Koeppl, Heinz
Title: Scalable Structure Learning of Continuous-Time Bayesian Networks from Incomplete Data
Language: English
Title of Book: Proceedings of Neural Information Processing Systems
Divisions: 18 Department of Electrical Engineering and Information Technology
18 Department of Electrical Engineering and Information Technology > Institute for Telecommunications > Bioinspired Communication Systems
18 Department of Electrical Engineering and Information Technology > Institute for Telecommunications
Event Title: 33rd Conference on Neural Information Processing Systems (NeurIPS 2019)
Event Location: Vancouver, Canada
Event Dates: 09.12.-13.12.2019
Date Deposited: 11 Sep 2019 12:27
Official URL: https://arxiv.org/abs/1909.04570
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