A Data Generation Method Combining Bayesian Networks and Expert Experience

This paper proposes an innovative data generation method aimed at producing highly realistic and relevant datasets. Building upon traditional Bayesian networks, we incorporate domain experts' knowledge and experience to enhance the model's predictive power and accuracy in specific areas. This method utilizes Bayesian networks to learn the structure of complex relationships and adjusts these relationships through expert experience, ultimately using the relationship model for data generation. In this study, the South China Sea military events are used as a case study to build a Bayesian network model capable of capturing complex interactions among these events. We demonstrate the effectiveness of this method in generating virtual samples related to specific events.