Engelmann, Nicolai ; Linzner, Dominik ; Koeppl, Heinz (2022)
Continuous Time Bayesian Networks with Clocks.
37th International Conference on Machine Learning. Online (12.07.2020-18.07.2020)
doi: 10.26083/tuprints-00021516
Konferenzveröffentlichung, Zweitveröffentlichung, Verlagsversion
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Kurzbeschreibung (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.
| Typ des Eintrags: | Konferenzveröffentlichung |
|---|---|
| Erschienen: | 2022 |
| Autor(en): | Engelmann, Nicolai ; Linzner, Dominik ; Koeppl, Heinz |
| Art des Eintrags: | Zweitveröffentlichung |
| Titel: | Continuous Time Bayesian Networks with Clocks |
| Sprache: | Englisch |
| Publikationsjahr: | 2022 |
| Ort: | Darmstadt |
| Publikationsdatum der Erstveröffentlichung: | 2020 |
| Verlag: | PMLR |
| Buchtitel: | Proceedings of the 37th International Conference on Machine Learning |
| Reihe: | Proceedings of Machine Learning Research |
| Band einer Reihe: | 119 |
| Veranstaltungstitel: | 37th International Conference on Machine Learning |
| Veranstaltungsort: | Online |
| Veranstaltungsdatum: | 12.07.2020-18.07.2020 |
| DOI: | 10.26083/tuprints-00021516 |
| URL / URN: | https://tuprints.ulb.tu-darmstadt.de/21516 |
| Zugehörige Links: | |
| Herkunft: | Zweitveröffentlichungsservice |
| Kurzbeschreibung (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: | Verlagsversion |
| URN: | urn:nbn:de:tuda-tuprints-215165 |
| Sachgruppe der Dewey Dezimalklassifikatin (DDC): | 000 Allgemeines, Informatik, Informationswissenschaft > 004 Informatik 600 Technik, Medizin, angewandte Wissenschaften > 620 Ingenieurwissenschaften und Maschinenbau |
| Fachbereich(e)/-gebiet(e): | 18 Fachbereich Elektrotechnik und Informationstechnik 18 Fachbereich Elektrotechnik und Informationstechnik > Institut für Nachrichtentechnik > Bioinspirierte Kommunikationssysteme 18 Fachbereich Elektrotechnik und Informationstechnik > Institut für Nachrichtentechnik 18 Fachbereich Elektrotechnik und Informationstechnik > Self-Organizing Systems Lab |
| Hinterlegungsdatum: | 20 Jul 2022 13:43 |
| Letzte Änderung: | 21 Jul 2022 13:28 |
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