10.5281/zenodo.5909504
https://zenodo.org/records/5909504
oai:zenodo.org:5909504
Nicolai Engelmann
Nicolai Engelmann
Department of Electrical Engineering and Information Technology, Technische Universitat Darmstadt, Darmstadt
Dominik Linzner
Dominik Linzner
Department of Electrical Engineering and Information Technology, Technische Universitat Darmstadt, Darmstadt
Heinz Koeppl
Heinz Koeppl
Department of Biology, Technische Universitat Darmstadt, Darmstadt, Germany
Continuous-Time Bayesian Networks with Clocks
Zenodo
2020
engineering
Continuous Time Bayesian Networks (CTBNs)
algorithms
Genes
Markovian
semi-Markov chains
2020-07-01
eng
https://arxiv.org/abs/2007.00347
10.5281/zenodo.5909503
https://zenodo.org/communities/ipc
https://zenodo.org/communities/eu
Creative Commons Attribution 4.0 International
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.
European Commission
10.13039/501100000780
826121
individualizedPaediatricCure: Cloud-based virtual-patient models for precision paediatric oncology