FBST for Mixture Model Selection.

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AIP Conference Proceedings 803:121-128 (2005)
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Abstract

The Fully Bayesian Significance Test (FBST) is a coherent Bayesian significance test for sharp hypotheses. This paper proposes the FBST as a model selection tool for general mixture models, and compares its performance with Mclust, a model-based clustering software. The FBST robust performance strongly encourages further developments and investigations.

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Julio Michael Stern
University of São Paulo

Citations of this work

Language and the Self-Reference Paradox.Julio Michael Stern - 2007 - Cybernetics and Human Knowing 14 (4):71-92.

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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.

Add more references