The complexity of MAP inference in Bayesian networks specified through logical languages (2015)
- Autores:
- Autores USP: MAUÁ, DENIS DERATANI - IME ; COZMAN, FABIO GAGLIARDI - EP
- Unidades: IME; EP
- Assuntos: COMPUTABILIDADE E COMPLEXIDADE; INTELIGÊNCIA ARTIFICIAL
- Idioma: Inglês
- Imprenta:
- Editora: AAAI Press / International Joint Conferences on Artificial Intelligence
- Local: Palo Alto
- Data de publicação: 2015
- Fonte:
- Título do periódico: Proceedings
- Nome do evento: International Joint Conference on Artificial Intelligence - IJCAI
-
ABNT
MAUÁ, Denis Deratani e CAMPOS, Cassio Polpo de e COZMAN, Fabio Gagliardi. The complexity of MAP inference in Bayesian networks specified through logical languages. 2015, Anais.. Palo Alto: AAAI Press / International Joint Conferences on Artificial Intelligence, 2015. Disponível em: http://ijcai.org/papers15/Papers/IJCAI15-130.pdf. Acesso em: 28 mar. 2024. -
APA
Mauá, D. D., Campos, C. P. de, & Cozman, F. G. (2015). The complexity of MAP inference in Bayesian networks specified through logical languages. In Proceedings. Palo Alto: AAAI Press / International Joint Conferences on Artificial Intelligence. Recuperado de http://ijcai.org/papers15/Papers/IJCAI15-130.pdf -
NLM
Mauá DD, Campos CP de, Cozman FG. The complexity of MAP inference in Bayesian networks specified through logical languages [Internet]. Proceedings. 2015 ;[citado 2024 mar. 28 ] Available from: http://ijcai.org/papers15/Papers/IJCAI15-130.pdf -
Vancouver
Mauá DD, Campos CP de, Cozman FG. The complexity of MAP inference in Bayesian networks specified through logical languages [Internet]. Proceedings. 2015 ;[citado 2024 mar. 28 ] Available from: http://ijcai.org/papers15/Papers/IJCAI15-130.pdf - On the complexity of propositional and relational credal networks
- On the semantics and complexity of probabilistic logic programs
- Credal sum-product networks
- DL-Lite Bayesian networks: a tractable probabilistic graphical model
- Specifying credal sets with probabilistic answer set programming
- The joy of probabilistic answer set programming: semantics, complexity, expressivity, inference
- The complexity of inferences and explanations in probabilistic logic programming
- The finite model theory of bayesian networks: descriptive complexity
- A tractable class of model counting problems
- Fast local search methods for solving limited memory influence diagrams
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