Abstract: Aiming at the problem of low accuracy of Bayesian network (BN) parameter learning results under the condition of small datasets, the necessity of variable weight design of BN parameters under the condition of small datasets is analyzed, and a BN parameter learning algorithm based on variable weight fusion, named VWPL, is proposed. Firstly, the inequality constraints are determined according to the expert experience, the minimum sample data set threshold is calculated and the variable weight factor function that changes with the sample size is designed. Then, the initial parameter set is calculated according to the sample, and the parameter expansion is carried out by the Bootstrap method to obtain the candidate parameter set that meets the constraints, and the final BN parameters can be obtained by substituting them into the BN variable weight parameter calculation model. The experimental results show that when the amount of learning data is small, the learning accuracy of the VWPL algorithm is higher than that of the MLE algorithm, the QMAP algorithm and the fixed-weight learning algorithm. In addition, the VWPL algorithm is successfully applied to the bearing fault diagnosis experiment, which provides a method of BN parameter estimation for small datasets. 

Key words: Bayesian network, small dataset, variable weight fusion, parameter learning