Prison term prediction of dangerous driving based on probabilistic graphical model

Abstract:

To satisfy the actual demand for interpretability and prediction accuracy in judicial practice, we in this paper propose an intelligent sentencing method based on the probabilistic graphical model (PGM). This model is built on the cornerstone of sentencing factors. The parameters are estimated by using the maximum likelihood criterion, and the predicted values are obtained by calculating the mathematical expectation of distribution. Experimental result on dangerous driving shows that the prediction accuracy of the method is better than that based on comparison models, such as decision tree and neural network. Meanwhile, this method has good interpretability as well.