  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
