Bayesian test of significance for conditional independence: The multinomial model

Pablo de Morais Andrade; Julio Michael Stern; Carlos Alberto de Bragança Pereira

doi:10.3390/e16031376

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arXiv · 1306.3627

Bayesian test of significance for conditional independence: The multinomial model

Pablo de Morais Andrade·Julio Michael Stern·Carlos Alberto de Bragança Pereira

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Abstract

Conditional independence tests (CI 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 (PGM)--which includes Bayesian Networks (BN) models--CI tests are especially important for the task of learning the PGM structure from data. In this paper, we propose the Full Bayesian Significance Test (FBST) for tests of conditional independence for discrete datasets. FBST is a powerful Bayesian test for precise hypothesis, as an alternative to frequentist's significance tests (characterized by the calculation of the \emph{p-value}).

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Pablo de Morais Andrade, Julio Michael Stern, Carlos Alberto de Bragança Pereira. 2013-06-16. Bayesian test of significance for conditional independence: The multinomial model. https://doi.org/10.3390/e16031376

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