Inference Complexity in Continuous Time Bayesian Networks

Liessman Sturlaugson Montana State University, John Sheppard Montana State University
Proceedings of the 30th Conference on Uncertainty in Artificial Intelligence, PMLR R12:324-331, 2014.

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.

Cite this Paper

BibTeX
@InProceedings{pmlr-vR12-university14k, title = {Inference Complexity in Continuous Time {B}ayesian Networks}, author = {University, Liessman Sturlaugson Montana State and University, John Sheppard Montana State}, booktitle = {Proceedings of the 30th Conference on Uncertainty in Artificial Intelligence}, pages = {324--331}, year = {2014}, editor = {Zhang, Nevin L. and Tian, Jin}, volume = {R12}, series = {Proceedings of Machine Learning Research}, month = {23--27 Jul}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/r12/main/assets/university14k/university14k.pdf}, url = {https://proceedings.mlr.press/r12/university14k.html}, 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.}, note = {Reissued by PMLR on 04 October 2026.} }
Endnote
%0 Conference Paper %T Inference Complexity in Continuous Time Bayesian Networks %A Liessman Sturlaugson Montana State University %A John Sheppard Montana State University %B Proceedings of the 30th Conference on Uncertainty in Artificial Intelligence %C Proceedings of Machine Learning Research %D 2014 %E Nevin L. Zhang %E Jin Tian %F pmlr-vR12-university14k %I PMLR %P 324--331 %U https://proceedings.mlr.press/r12/university14k.html %V R12 %X 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. %Z Reissued by PMLR on 04 October 2026.
APA
University, L.S.M.S. & University, J.S.M.S.. (2014). Inference Complexity in Continuous Time Bayesian Networks. Proceedings of the 30th Conference on Uncertainty in Artificial Intelligence, in Proceedings of Machine Learning Research R12:324-331 Available from https://proceedings.mlr.press/r12/university14k.html. Reissued by PMLR on 04 October 2026.

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