Sum-Product Networks: A Survey
A sum-product network (SPN) is a probabilistic model, based on a rooted acyclic directed graph, in which terminal nodes represent probability distributions and non-terminal nodes represent convex sums (weighted averages) and products of probability distributions. They are closely related to probabil...
| Autores: | , , |
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| Tipo de documento: | artigo |
| Data de publicação: | 2021 |
| País: | España |
| Recursos: | Universidad Nacional de Educación a Distancia |
| Repositório: | e-spacio. Repositorio Institucional de la UNED |
| Idioma: | inglês |
| OAI Identifier: | oai:e-spacio.uned.es:20.500.14468/12464 |
| Acesso em linha: | https://hdl.handle.net/20.500.14468/12464 |
| Access Level: | Acceso aberto |
| Palavra-chave: | Sum-product networks probabilistic graphical models Bayesian networks machine learning deep neural networks |
| Resumo: | A sum-product network (SPN) is a probabilistic model, based on a rooted acyclic directed graph, in which terminal nodes represent probability distributions and non-terminal nodes represent convex sums (weighted averages) and products of probability distributions. 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 edges 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, several applications, a brief review of software libraries, and a comparison with related models. |
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