Fast factorisation of probabilistic potentials and its application to approximate inference in Bayesian networks

We present an efficient procedure for factorising probabilistic potentials represented as probability trees. This new procedure is able to detect some regularities that cannot be captured by existing methods. In cases where an exact decomposition is not achievable, we propose a heuristic way to c...

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Detalhes bibliográficos
Principais autores: Cano, Andrés, Gómez Olmedo, Manuel, Pérez-Ariza, Cora B., Salmerón Cerdán, Antonio
Formato: info:eu-repo/semantics/article
Idioma:English
Publicado em: 2017
Assuntos:
Acesso em linha:http://hdl.handle.net/10835/4885
https://doi.org/10.1142/S0218488512500110
Descrição
Resumo:We present an efficient procedure for factorising probabilistic potentials represented as probability trees. This new procedure is able to detect some regularities that cannot be captured by existing methods. In cases where an exact decomposition is not achievable, we propose a heuristic way to carry out approximate factorisations guided by a parameter called factorisation degree, which is fast to compute. We show how this parameter can be used to control the tradeoff between complexity and accuracy in approximate inference algorithms for Bayesian networks.