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)
OBJECTIVES To investigate the effect of using osteogenic induced gingival fibroblasts (OIGFs) and low intensity pulsed ultrasound (LIPUS) on root resorption lacunae volume and cementum thickness in beagle dogs that received orthodontic tooth movement. MATERIALS AND METHODS Seven beagle dogs were used, from which gingival cells (GCs) were obtained and were(More)
One of the main issues that have emerged from learning Bayesian networks from data is the need for computational efficiency. In recent years, it has been shown that exact probabilistic propagation algorithms can be used for a quick and efficient absorption of information on dynamic junction trees of cliques. These algorithms were applied on Gaussian(More)
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