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We introduce the minimum description length (MDL) principle, a general principle for inductive inference based on the idea that regularities (laws) underlying data can always be used to compress data. We introduce the fundamental concept of MDL, called the stochastic complexity, and we show how it can be used for model selection. We briefly compare MDL-based model selection to other approaches and we informally explain why we may expect MDL to give good results in practical applications.doi:10.1006/jmps.1999.1280 pmid:10733861 fatcat:gst6rgndnjawvkt43yllf3kgv4