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Strong asymptotic assertions for discrete MDL in regression and classification

Poland, Jan; Hutter, Marcus


We study the properties of the MDL (or maximum penalized complexity) estimator for Regression and Classification, where the underlying model class is countable. We show in particular a finite bound on the Hellinger losses under the only assumption that there is a ``true'' model contained in the class. This implies almost sure convergence of the predictive distribution to the true ...[Show more]

CollectionsANU Research Publications
Date published: 2005-02
Type: Conference paper


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