【摘要】 随着贝叶斯网成为一种不确定知识表示和推理的工具,它逐渐被应用到各式各样的领域中.面对实际的问题,贝叶斯网规模不断扩大、复杂程度也不断提高.直接处理这样的模型是不现实的.将复杂的模型无损地分解成更小的模型就是一种解决问题的办法.基于边缘模型及其性质,给出一种简单的贝叶斯网无损分解方法.更多还原

【Abstract】 As Bayesian Networks becomes popular tools for common knowledge representation and reasoning of partial beliefs under uncertainty,Bayesian Networks have been successfully applied to a variety of problem domains.Confronted with many real-world applications,Bayesian Networks established become larger and more complex.It is not realistic to infer directly on these models.Thus,losslessly decomposing large and complex Bayesian Networks into smaller submodels is to be an alternative solution.Based on the properties of marginal models.A simpler method to losslessly decompose the Bayesian network into a set of smaller Bayesian networks is given.更多还原