Testing Significance in Bayesian Classifiers.

Frontiers in Artificial Intelligence and Applications 132:34-41 (2005)
  Copy   BIBTEX


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

Julio Michael Stern
University of São Paulo


Added to PP

90 (#83,513)

6 months
43 (#79,064)

Historical graph of downloads since first upload
This graph includes both downloads from PhilArchive and clicks on external links on PhilPapers.
How can I increase my downloads?