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• W1973217014 abstract "This article reviews the principle of minimum description length (MDL) for problems of model selection. By viewing statistical modeling as a means of generating descriptions of observed data, the MDL framework discriminates between competing models based on the complexity of each description. This approach began with Kolmogorov's theory of algorithmic complexity, matured in the literature on information theory, and has recently received renewed attention within the statistics community. Here we review both the practical and the theoretical aspects of MDL as a tool for model selection, emphasizing the rich connections between information theory and statistics. At the boundary between these two disciplines we find many interesting interpretations of popular frequentist and Bayesian procedures. As we show, MDL provides an objective umbrella under which rather disparate approaches to statistical modeling can coexist and be compared. We illustrate the MDL principle by considering problems in regression, nonpar..." @default.
• W1973217014 created "2016-06-24" @default.
• W1973217014 creator A5023047996 @default.
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• W1973217014 date "2001-06-01" @default.
• W1973217014 modified "2023-10-13" @default.
• W1973217014 title "Model Selection and the Principle of Minimum Description Length" @default.
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• W1973217014 doi "https://doi.org/10.1198/016214501753168398" @default.
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