Abstract:We leverage the Minimum Description Length (MDL) principle as a model selection technique for Bernoulli distributions and compare several types of MDL codes. We first present a simplistic crude two-part MDL code and a Normalized Maximum Likelihood (NML) code. We then focus on the enumerative two-part crude MDL code, suggest a Bayesian interpretation for finite size data samples, and exhibit a strong connection with the NML approach. We obtain surprising impacts on the estimation of the model complexity togethe… Show more
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