A. S. Gargoum

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In this paper we extend the work of Smith and Papamichail (1999) and present fast approximate Bayesian algorithms for learn­ ing in complex scenarios where at any time frame, the relationships between explanatory state space variables can be described by a Bayesian network that evolve dynamically over time and the observations taken are not necessarily(More)
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