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Filtros : "International Journal of Approximate Reasoning" "2016" Limpar

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  • Artigo de periodico

    Equivalences between maximum a posteriori inference in Bayesian networks and maximum expected utility computation in influence diagrams (2016)

    Mauá, Denis Deratani

    Fonte: International Journal of Approximate Reasoning. Unidade: IME

    Assuntos: INTELIGÊNCIA ARTIFICIAL, PROBABILIDADE APLICADA, RACIOCÍNIO PROBABILÍSTICO

    Acesso à fonteDOIComo citar
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    • ABNT

      MAUÁ, Denis Deratani. Equivalences between maximum a posteriori inference in Bayesian networks and maximum expected utility computation in influence diagrams. International Journal of Approximate Reasoning, v. 68, n. Ja 2016, p. 211-229, 2016Tradução . . Disponível em: https://doi.org/10.1016/j.ijar.2015.03.007. Acesso em: 09 nov. 2025.

    • APA

      Mauá, D. D. (2016). Equivalences between maximum a posteriori inference in Bayesian networks and maximum expected utility computation in influence diagrams. International Journal of Approximate Reasoning, 68( Ja 2016), 211-229. doi:10.1016/j.ijar.2015.03.007

    • NLM

      Mauá DD. Equivalences between maximum a posteriori inference in Bayesian networks and maximum expected utility computation in influence diagrams [Internet]. International Journal of Approximate Reasoning. 2016 ; 68( Ja 2016): 211-229.[citado 2025 nov. 09 ] Available from: https://doi.org/10.1016/j.ijar.2015.03.007

    • Vancouver

      Mauá DD. Equivalences between maximum a posteriori inference in Bayesian networks and maximum expected utility computation in influence diagrams [Internet]. International Journal of Approximate Reasoning. 2016 ; 68( Ja 2016): 211-229.[citado 2025 nov. 09 ] Available from: https://doi.org/10.1016/j.ijar.2015.03.007

  • Artigo de periodico

    Fast local search methods for solving limited memory influence diagrams (2016)

    Mauá, Denis Deratani ; Cozman, Fabio Gagliardi

    Fonte: International Journal of Approximate Reasoning. Unidades: IME, EP

    Assuntos: MODELOS PARA PROCESSOS ESTOCÁSTICOS, INTELIGÊNCIA ARTIFICIAL, PROBABILIDADE APLICADA

    PrivadoAcesso à fonteDOIComo citar
    A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas

    • ABNT

      MAUÁ, Denis Deratani e COZMAN, Fabio Gagliardi. Fast local search methods for solving limited memory influence diagrams. International Journal of Approximate Reasoning, v. 68, n. Ja 2016, p. 230-245, 2016Tradução . . Disponível em: https://doi.org/10.1016/j.ijar.2015.05.003. Acesso em: 09 nov. 2025.

    • APA

      Mauá, D. D., & Cozman, F. G. (2016). Fast local search methods for solving limited memory influence diagrams. International Journal of Approximate Reasoning, 68( Ja 2016), 230-245. doi:10.1016/j.ijar.2015.05.003

    • NLM

      Mauá DD, Cozman FG. Fast local search methods for solving limited memory influence diagrams [Internet]. International Journal of Approximate Reasoning. 2016 ; 68( Ja 2016): 230-245.[citado 2025 nov. 09 ] Available from: https://doi.org/10.1016/j.ijar.2015.05.003

    • Vancouver

      Mauá DD, Cozman FG. Fast local search methods for solving limited memory influence diagrams [Internet]. International Journal of Approximate Reasoning. 2016 ; 68( Ja 2016): 230-245.[citado 2025 nov. 09 ] Available from: https://doi.org/10.1016/j.ijar.2015.05.003
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