Sum-product networks(SPNs)are deep probabilistic graphical models based on rooted directed acyclic graphs.Except for leaf nodes,the remaining nodes are composed of summation nodes or quadrature nodes.Sum-product networks are closely related to probabilistic graph models,but the calculation process of sum-product networks only in-volves simple network polynomial summation and quadrature operations,and can achieve accurate and approximate rea-soning.Compared with classical probability graph models,sum-product networks can construct models that are easy to reason from training data.In addition,sum-product networks can also be used as deep learning models similar to neural networks.This paper mainly elaborates on the basic principles,theoretical research,learning techniques,variant models,and specific applications in various fields of sum-product networks.Firstly,the basic principles of sum-product networks are summarized,including the current research status of sum-product network theory.Secondly,several types of variant models of sum-product networks are summarized,and learning algorithms for parameter learning and structure learning in sum-product network learning techniques are summarized.In addition,we have also outlined sum-product network based application models in specific application fields such as natural language processing,speech recognition,and medical re-search.Finally,based on the existing research foundation,the future development trends and directions of sum-product networks are prospected.
sum-product networklearn techniques of SPNsprobabilistic graphical model:deep learning
万方数据
维普
