Tutorial and Selected Approaches on Parameter Learning in Bayesian Network with Incomplete Data

Bayesian networks (BN) are used in a big range of applications but they have one issue concerning parameter learning. In real application, training data are always incomplete or some nodes are hidden. To deal with this problem many learning parameter algorithms are suggested foreground EM, Gibbs sampling and RBE algorithms. This paper presents a tutorial of basic concepts and in particular techniques and algorithms associated with learning in Bayesian network with incomplete data. We present also selected applications in the fields.

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