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Discrete MDL predicts in total variation

Hutter, Marcus


The Minimum Description Length (MDL) principle selects the model that has the shortest code for data plus model. We show that for a countable class of models, MDL predictions are close to the true distribution in a strong sense. The result is completely general. No independence, ergodicity, stationarity, identifiability, or other assumption on the model class need to be made. More formally, we show that for any...[Show more]

CollectionsANU Research Publications
Date published: 2009-12
Type: Conference paper


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