Sum-product networks: A survey
It offers a comprehensive overview for researchers and practitioners interested in tractable probabilistic modeling, but it is incremental as it summarizes existing work without introducing new methods.
This paper provides a survey of sum-product networks (SPNs), which are probabilistic models designed for tractable inference tasks, such as image processing and natural language understanding, by enabling inference in time proportional to the graph size.
A sum-product network (SPN) is a probabilistic model, based on a rooted acyclic directed graph, in which terminal nodes represent univariate probability distributions and non-terminal nodes represent convex combinations (weighted sums) and products of probability functions. They are closely related to probabilistic graphical models, in particular to Bayesian networks with multiple context-specific independencies. Their main advantage is the possibility of building tractable models from data, i.e., models that can perform several inference tasks in time proportional to the number of links in the graph. They are somewhat similar to neural networks and can address the same kinds of problems, such as image processing and natural language understanding. This paper offers a survey of SPNs, including their definition, the main algorithms for inference and learning from data, the main applications, a brief review of software libraries, and a comparison with related models