Authors
Julio Michael Stern
University of São Paulo
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.
Keywords Mixture models, Bayesian classification, significance tests
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References found in this work BETA

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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FBST for Mixture Model Selection.Julio Michael Stern & Marcelo de Souza Lauretto - 2005 - AIP Conference Proceedings 803:121-128.
Paraconsistent Sensitivity Analysis for Bayesian Significance Tests.Julio Michael Stern - 2004 - Lecture Notes in Artificial Intelligence 3171:134-143.
Significance Testing in Theory and Practice.Daniel Greco - 2011 - British Journal for the Philosophy of Science 62 (3):607-637.

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