Prediction Of Criminal Sentence for Dangerous Driving Based on Probability Graph Model

Abstract

In order to improve the accuracy and efficiency of predicting the sentence for the crime of dangerous driving, this article analyzes the application of probability graph models in judicial reasoning. By constructing an accurate model of complex evidence relationships and integrating uncertainty information in court evidence, the results show that the model can effectively support judgments and improve the transparency and fairness of judicial decision-making. In addition, the interpretability of the model provides legal professionals with a clear view of the decision-making process, enhancing the scientific and rational nature of legal judgments.