Preprint Open Access

Continuous-Time Bayesian Networks with Clocks

Engelmann, Nikolai; Linzner, Dominik; Koeppl, Heinz

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.

Files (1.5 MB)
Name Size
1.5 MB Download
All versions This version
Views 3232
Downloads 2727
Data volume 40.2 MB40.2 MB
Unique views 3232
Unique downloads 2727


Cite as