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Lecture Notes in Computer Science
We consider the Minimum Description Length principle for online sequence prediction. If the underlying model class is discrete, then the total expected square loss is a particularly interesting performance measure: (a) this quantity is bounded, implying convergence with probability one, and (b) it additionally specifies a rate of convergence. Generally, for MDL only exponential loss bounds hold, as opposed to the linear bounds for a Bayes mixture. We show that this is even the case if the modeldoi:10.1007/978-3-540-30215-5_23 fatcat:smdaoeqcjzafrkuhrpf5wtomoi