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Asymptotics of discrete (MDL) for online prediction

Poland, Jan; Hutter, Marcus


Minimum description length (MDL) is an important principle for induction and prediction, with strong relations to optimal Bayesian learning. This paper deals with learning processes which are independent and identically distributed (i.i.d.) by means of two-part MDL, where the underlying model class is countable. We consider the online learning framework, i.e., observations come in one by one, and the predictor is allowed to update its state of mind after each time step. We identify two ways of...[Show more]

CollectionsANU Research Publications
Date published: 2005
Type: Journal article
Source: IEEE Transactions on Information Theory
DOI: 10.1109/TIT.2005.856956


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