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Master's Dissertation
DOI
10.11606/D.45.2013.tde-27082013-111753
Document
Author
Full name
Viviane Teles de Lucca Maranhão
E-mail
Institute/School/College
Knowledge Area
Date of Defense
Published
São Paulo, 2013
Supervisor
Committee
Stern, Julio Michael (President)
Cozman, Fabio Gagliardi
Gubitoso, Marco Dimas
Title in Portuguese
Estudo de técnicas de paralelização de métodos computacionais de fatoração de matrizes esparsas aplicados à redes bayesianas e redes credais
Keywords in Portuguese
Computação Paralela
Matrizes Esparsas
Redes Bayesianas
Redes Credais
Abstract in Portuguese
Neste trabalho demos continuidade ao estudo desenvolvido por Colla (2007) que utilizou-se do arcabouço de álgebra linear com técnicas de fatoração de matrizes esparsas aplicadas à inferência em redes Bayesianas. Com isso, a biblioteca computacional resultante possui uma separação clara entre a fase simbólica e numérica da inferência, o que permite aproveitar os resultados obtidos na primeira etapa para variar apenas os valores numéricos. Aplicamos técnicas de paralelização para melhorar o desempenho computacional, adicionamos inferência para Redes Credais e novos algoritmos para inferência em Redes Bayesianas para melhor eciência dependendo da estrutura do grafo relacionado à rede e buscamos tornar ainda mais independentes as etapas simbólica e numérica.
Title in English
Study of parallelization techniques of computational methods for sparse matrix factorization applied to Bayesian and credal networks
Keywords in English
Bayesian Networks
Credal Networks
Parallel Computing
Sparse Matrixes
Abstract in English
In this work we continued the study by Colla (2007), who used the framework of linear algebra techniques with sparse matrix factorization applied to inference in Bayesian networks. Thus, the resulting computational library has a clear separation between the symbolic and numerical phase of inference, which allows you to use the results obtained in the rst step to vary only numeric values. We applied parallelization techniques to improve computational performance, we add inference to Credal Networks and new algorithms for inference in Bayesian networks for better eciency depending on the structure of the graph related to network and seek to become more independent symbolic and numerical steps.
 
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Publishing Date
2013-09-05
 
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