基于贝叶斯和蒙特卡罗的多信号模型扩展研究

传统多信号模型基于确定性测试假设条件，忽略了系统存在不确定性的真实情况，在传统多信号模型基础上引入贝叶斯条件概率来表示不确定性问题，并通过蒙特卡罗方法进行仿真模拟，将不确定性问题转化为单次试验确定性问题，进而使用相关矩阵进行测试性分析，通过程序实现和算例验证了该方法的有效性，并可以根据反馈数据进行参数学习，修正初始条件概率。

Research on multi-signal model expansion based on Bayesian and Monte Carlo

Traditional multi-signal model based on the condition of deterministic assumption, lgnore the fact that the system is uncertain. The Bayesain condition probability is introduced into the traditional multi-signal model to express the uncertainty, and then using the Monte Carlo method to simulate the uncertainty problem, and transform it into a single deterministic problem, Finally, the correlation matrix is used to analyze the testability. The availability of the method is validated by program implementation and example analysis, and the initial condition probability can be corrected by parameter learning according to the feedback data.