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 |
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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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