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Continuous Time Bayesian Networks with Clocks

Engelmann, Nicolai ; Linzner, Dominik ; Koeppl, Heinz (2022)
Continuous Time Bayesian Networks with Clocks.
37th International Conference on Machine Learning. Online (12.-18.07.2020)
doi: 10.26083/tuprints-00021516
Conference or Workshop Item, Secondary publication, Publisher's Version

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Item Type: Conference or Workshop Item
Type of entry: Secondary publication
Title: Continuous Time Bayesian Networks with Clocks
Language: English
Date: 2022
Place of Publication: Darmstadt
Year of primary publication: 2020
Publisher: PMLR
Book Title: Proceedings of the 37th International Conference on Machine Learning
Series: Proceedings of Machine Learning Research
Series Volume: 119
Event Title: 37th International Conference on Machine Learning
Event Location: Online
Event Dates: 12.-18.07.2020
DOI: 10.26083/tuprints-00021516
Corresponding Links:
Origin: Secondary publication service
Abstract:

Structured stochastic processes evolving in continuous time present a widely adopted framework to model phenomena occurring in nature and engineering. However, such models are often chosen to satisfy the Markov property to maintain tractability. One of the more popular of such memoryless models are Continuous Time Bayesian Networks (CTBNs). In this work, we lift its restriction to exponential survival times to arbitrary distributions. Current extensions achieve this via auxiliary states, which hinder tractability. To avoid that, we introduce a set of node-wise clocks to construct a collection of graph-coupled semi-Markov chains. We provide algorithms for parameter and structure inference, which make use of local dependencies and conduct experiments on synthetic data and a data-set generated through a benchmark tool for gene regulatory networks. In doing so, we point out advantages compared to current CTBN extensions.

Status: Publisher's Version
URN: urn:nbn:de:tuda-tuprints-215165
Classification DDC: 000 Generalities, computers, information > 004 Computer science
600 Technology, medicine, applied sciences > 620 Engineering and machine engineering
Divisions: 18 Department of Electrical Engineering and Information Technology > Institute for Telecommunications > Bioinspired Communication Systems
18 Department of Electrical Engineering and Information Technology > Self-Organizing Systems Lab
Date Deposited: 20 Jul 2022 13:43
Last Modified: 12 Apr 2023 07:49
URI: https://tuprints.ulb.tu-darmstadt.de/id/eprint/21516
PPN: 497909405
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