Suboptimal behaviour of Bayes and MDL in classification under misspecification
Peter Grünwald and John Langford
Machine Learning Volume 66, Number 2-3, pp. 119-149, 2007.

This is the latest version of this eprint.

Abstract

We show that forms of Bayesian and MDL inference that are often applied to classification problems can be {\em inconsistent}. This means that there exists a learning problem such that for all amounts of data the generalization errors of the MDL classifier and the Bayes classifier relative to the Bayesian posterior both remain bounded away from the smallest achievable generalization error. We extensively discuss the result from both a Bayesian and an MDL perspective.

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• Suboptimal behaviour of Bayes and MDL in classification under misspecification (deposited 28 November 2005)
  • Suboptimal behaviour of Bayes and MDL in classification under misspecification (deposited 07 February 2008) [Currently Displayed]

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