2016/08/19 by Marc Boullé, Boullé, Marc, Fabrice Clérot +3
Computer Science · #68P30 #Algorithms and Data Compression #Bayesian Methods and Mixture Models #E.4 #FOS: Computer and information sciences #Gaussian Processes and Bayesian Inference #Information Theory (cs.IT) #Machine Learning and Algorithms
paper · pdf · doi:10.48550/arxiv.1608.05522
openalex publication_date 2016/08/19 · openalex created_date 2022/09/02 · openalex updated_date 2026/07/28
We leverage the Minimum Description Length (MDL) principle as a model\nselection technique for Bernoulli distributions and compare several types of\nMDL codes. We first present a simplistic crude two-part MDL code and a\nNormalized Maximum Likelihood (NML) code. We then focus on the enumerative\ntwo-part crude MDL code, suggest a Bayesian interpretation for finite size data\nsamples, and exhibit a strong connection with the NML approach. We obtain\nsurprising impacts on the estimation of the model complexity together with\nsuperior compression performance. This is then generalized to the case of the\nmultinomial distributions. Both the theoretical analysis and the experimental\ncomparisons suggest that one might use the enumerative code rather than NML in\npractice, for Bernoulli and multinomial distributions.\n