  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 and RBE algorithms. This algorithm
incorporates the range of a parameter into the EM algorithm. This range is
calculated by the first step of RBE algorithm allowing a regularization of each
parameter in bayesian network after the maximization step of the EM algorithm.
The threshold EM algorithm is applied in brain tumor diagnosis and show some
advantages and disadvantages over the EM algorithm.
