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Testing Significance in Bayesian Classifiers.
Frontiers in Artificial Intelligence and Applications 132:34-41 (2005)
Abstract
The Fully Bayesian Significance Test (FBST) is a coherent Bayesian significance test for sharp hypotheses. This paper explores the FBST as a model selection tool for general mixture models, and gives some computational experiments for Multinomial-Dirichlet-Normal-Wishart models.Author's Profile
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Citations of this work
The Full Bayesian Significance Test for Mixture Models: Results in Gene Expression Clustering.Julio Michael Stern, Marcelo de Souza Lauretto & Carlos Alberto de Braganca Pereira - 2008 - Genetics and Molecular Research 7 (3):883-897.
References found in this work
Bayesian Evidence Test for Precise Hypotheses.Julio Michael Stern - 2003 - Journal of Statistical Planning and Inference 117 (2):185-198.
Paraconsistent Sensitivity Analysis for Bayesian Significance Tests.Julio Michael Stern - 2004 - Lecture Notes in Artificial Intelligence 3171:134-143.
Significance Tests, Belief Calculi, and Burden of Proof in Legal and Scientific Discourse.Julio Michael Stern - 2003 - Frontiers in Artificial Intelligence and Applications 101:139-147.
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