Learning BN parameters with small data sets based by data reutilization

In this paper, parameters learning of discrete Bayesian networks (BNs) with small data sets with convex constraints is investigated, and the main task is improving the accuracy of parameter learning through offsetting the lack of data with prior knowledge. Data and prior knowledge are often mechanically integrated in most existing algorithms because they are treated independent. However, after a theoretical study, they are found dependent on each other, and the existing algorithms have dissipated this relevance. A novel parameter learning algorithm-Bayesian estimation based on data reutilization under convex constraints, is proposed with deeply mining the information between data and prior knowledge based on classification of data information. Finally, simulations demonstrate the advantages of novel algorithm in precision and other indexes, which in turn tells the dependance between data and prior information.