Hybrid time Bayesian networks

Manxia Liu, Arjen Hommersom, Maarten van der Heijden*, Peter J.F. Lucas

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

11 Citations (Scopus)


Capturing heterogeneous dynamic systems in a probabilistic model is a challenging problem. A single time granularity, such as employed by dynamic Bayesian networks, provides insufficient flexibility to capture the dynamics of many real-world processes. The alternative is to assume that time is continuous, giving rise to continuous time Bayesian networks. Here the problem is that the level of temporal detail is too precise to match available probabilistic knowledge. In this paper, we present a novel class of models, called hybrid time Bayesian networks, which combine discrete-time and continuous-time Bayesian networks. The new formalism allows us to more naturally model dynamic systems with regular and irregularly changing variables. We also present a mechanism to construct discrete-time versions of hybrid models and an EM-based algorithm to learn the parameters of the resulting BNs. Its usefulness is illustrated by means of a real-world medical problem.

Original languageEnglish
Pages (from-to)460-474
Number of pages15
JournalInternational Journal of Approximate Reasoning
Publication statusPublished - 1 Jan 2017
Externally publishedYes


  • Continuous time Bayesian networks
  • Dynamic Bayesian networks
  • Dynamic systems
  • n/a OA procedure


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