Accelerated quantification of Bayesian networks with incomplete data
Bo Thiesson · 1995
Probabilistic expert systems based on Bayesian networks (BNs) require initial specification both a qualitative graphical structure and quan-titative assessment of conditional probability ta-bles. This paper considers statistical batch learn-ing of the probability tables on the basis of in-complete data and expert knowledge. The EM algorithm with a generalized conjugate gradient acceleration method has been dedicated to quan-tification of BNs by maximum posterior likeli-hood estimation for a super-class of the recursive graphical models. This new class of models al-lows a great variety of local functional restrictions to be imposed on the statistical model, which hereby extents the control and applicability of the constructed method for quantifying BNs.