Suboptimal behaviour of Bayes and MDL in classification under misspecification

Peter Grunwald; John Langford

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arXiv · math/0406221

Suboptimal behaviour of Bayes and MDL in classification under misspecification

Peter Grunwald·John Langford

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Abstract

We show that forms of Bayesian and MDL inference that are often applied to classification problems can be *inconsistent*. This means 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.

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Peter Grunwald, John Langford. 2004-06-10. Suboptimal behaviour of Bayes and MDL in classification under misspecification. https://arxiv.org/abs/math/0406221

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