Abstract
It is widely recognized that probabilistic graphical models provide a good framework for both knowledge representation and probabilistic inference (e.g., see [Cheeseman94], [Whittaker90]). The dynamic behaviour of a system which changes over time requires an implicit or explicit time representation. In this paper, an implicit time representation using dynamic graphical models is proposed. Our goal is to model the state of a system and its evolution over time in a richer and more natural way than other approaches together with a more suitable treatment of the inference on variables of interest.
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Lekuona, A., Lacruz, B., Lasala, P. (1996). Modeling and Monitoring Dynamic Systems by Chain Graphs. In: Fisher, D., Lenz, HJ. (eds) Learning from Data. Lecture Notes in Statistics, vol 112. Springer, New York, NY. https://doi.org/10.1007/978-1-4612-2404-4_7
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DOI: https://doi.org/10.1007/978-1-4612-2404-4_7
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