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Bayesian Test of Significance for Conditional Independence: The Multinomial Model

Instituto de Matemática e Estatística, Universidade de São Paulo (IME-USP) Rua do Matão, 1010, Cidade Universitária, São Paulo, SP/Brasil
Author to whom correspondence should be addressed.
Entropy 2014, 16(3), 1376-1395;
Received: 3 December 2013 / Revised: 21 February 2014 / Accepted: 5 March 2014 / Published: 7 March 2014
Conditional independence tests have received special attention lately in machine learning and computational intelligence related literature as an important indicator of the relationship among the variables used by their models. In the field of probabilistic graphical models, which includes Bayesian network models, conditional independence tests are especially important for the task of learning the probabilistic graphical model structure from data. In this paper, we propose the full Bayesian significance test for tests of conditional independence for discrete datasets. The full Bayesian significance test is a powerful Bayesian test for precise hypothesis, as an alternative to the frequentist’s significance tests (characterized by the calculation of the p-value). View Full-Text
Keywords: hypothesis testing; probabilistic graphical models hypothesis testing; probabilistic graphical models
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MDPI and ACS Style

De Morais Andrade, P.; Stern, J.M.; De Bragança Pereira, C.A. Bayesian Test of Significance for Conditional Independence: The Multinomial Model. Entropy 2014, 16, 1376-1395.

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