Bayesian network is becoming more remarkable in AI research fields

which plays important role in modern expert system

diagnoses system and decision system.Bayesian network works on three points as below:knowledge representation

learning and inference.Prababilistic methods are its mathematical fundamental which helps learning distribution from data and leads Bayesian theory to real application.This paper introduces various common methods in probability data learning and make comparison among them under various application background.The methods based on classical statistics have a matured theory and a set of simple and direct calculation.But they reply heavily on sample data

which apply only those information from sample data with expert knowledge left aside.Bayesian Network combines information of expert knowledge and sample data together.It can give more accurate learning result and rely less on sample data.Parameter learning is main part of learning Bayesian Network models

and it's the basis of Bayesian Network learning.