Abstract
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. Its usefulness is illustrated by means of a real-world medical problem.
ML is supported by China Scholarship Council. AH and MVDH are supported by the ITEA2 MoSHCA project (ITEA2-ip11027).
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Liu, M., Hommersom, A., van der Heijden, M., Lucas, P.J.F. (2015). Hybrid Time Bayesian Networks. In: Destercke, S., Denoeux, T. (eds) Symbolic and Quantitative Approaches to Reasoning with Uncertainty. ECSQARU 2015. Lecture Notes in Computer Science(), vol 9161. Springer, Cham. https://doi.org/10.1007/978-3-319-20807-7_34
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DOI: https://doi.org/10.1007/978-3-319-20807-7_34
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