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The threshold EM algorithm for parameter learning in bayesian network with incomplete data
[article]
2012
arXiv
pre-print
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. In order to limit the search space and escape from local maxima produced by executing EM algorithm, this paper presents a learning parameter algorithm that is a fusion of EM
arXiv:1204.1681v1
fatcat:7kqyiwf7tfel7jc4zbue42fmhy