Bayesian Network-Based Knowledge Graph Inference for Highway Transportation Safety Risks

Algorithm 2

	Input: observation variable Y, hidden variable Z, joint distribution , conditional probability distribution .
	Output: model parameter
(1)	Assignment of the initial values for the model parameter;
(2)	E-step: is the value of the model parameter after the th iteration, the calculated function of expectation on i + 1 th iteration,
(3)	M-step: find that maximizes , determine the estimated value of the i + 1 th iteration.
(4)	Repeat steps 2-3 until model converges.