Uncertainty in Artificial Intelligence
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Inference Complexity in Continuous Time Bayesian Networks
Liessman Sturlaugson, John Sheppard
Abstract:
The continuous time Bayesian network (CTBN) enables temporal reasoning by rep- resenting a system as a factored, finite-state Markov process. The CTBN uses a tra- ditional Bayesian network (BN) to specify the initial distribution. Thus, the complex- ity results of Bayesian networks also apply to CTBNs through this initial distribution. However, the question remains whether prop- agating the probabilities through time is, by itself, also a hard problem. We show that exact and approximate inference in continu- ous time Bayesian networks is NP-hard even when the initial states are given.
Keywords:
Pages: 772-779
PS Link:
PDF Link: /papers/14/p772-sturlaugson.pdf
BibTex:
@INPROCEEDINGS{Sturlaugson14,
AUTHOR = "Liessman Sturlaugson and John Sheppard",
TITLE = "Inference Complexity in Continuous Time Bayesian Networks",
BOOKTITLE = "Proceedings of the Thirtieth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-14)",
PUBLISHER = "AUAI Press",
ADDRESS = "Corvallis, Oregon",
YEAR = "2014",
PAGES = "772--779"
}


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