The Minimum Description Length Principle and Reasoning.
The Minimum Description Length (MDL) Principle, which was originally proposed by Jorma Rissanen in 1978 as a computable approximation of Kolmogorov complexity, is a powerful method for inductive inference.
The Minimum Description Length Principle and Reasoning under Uncertainty. PhD thesis, University of Amsterdam, 1998. ILLC Dissertation series 1998-03. Google Scholar; Michael Isard and Andrew Blake. CONDENSATION - conditional density propagation for visual tracking. Int. J. Computer Vision, 29(1):5-28, 1998. Google Scholar Digital Library.
P. Luosto and P. Kontkanen, Clustgrams: an extension to histogram densities based on the minimum description length principle. Central European Journal of Computer Science 1 (2011) 4, 466-481. Central European Journal of Computer Science 1 (2011) 4, 466-481.
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A Note on the Applied Use of MDL Approximations Daniel J. Navarro Department of Psychology Ohio State University Abstract An applied problem is discussed in which two nested psychological models of retention are compared using Minimum Description Length (MDL).
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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.
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The Minimum Description Length Principle and Reasoning under Uncertainty. Ph.D. Thesis, ILLC Dissertation Series DS 1998-03, CWI, the Netherlands. 1998. Quick Clustering Algorithms.
The MDL Principle has mainly been developed by J. Rissanen in a series of papers. thereby uncovering a single general, under-lying principle, formulated in Figure 2.4. Therefore, if one understands the material in this section, then one understands the Minimum Description Length Principle. First, Section 2.7.1, we show how to compare.
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A Fast Normalized Maximum Likelihood Algorithm for Multinomial Data.. CWI, ILLC Dissertation Series 1998-03, 1998.. The minimum description length (MDL) principle is a theoretically well.
COMPUTING THE REGRET TABLE FOR MULTINOMIAL DATA.. Length Principle and Reasoning under Uncertainty. PhD. thesis, CWI, ILLC Dissertation Series 1998-03, 1998. (Johnson et al., 1997).
ILLCC DS-1998-01: Sebastiaan A. Terwijn ComputabUityComputabUity and Measure ILLCC DS-1998-02: Sjoerd D. Zwart ApproachApproach to the Truth: Verisimilitude and Truthlikeness ILLCC DS-1998-03: Peter Grunwald TheThe Minimum Description Length Principle and Reasoning under Uncertainty ILLCC DS-1998-04: Giovanna d'Agostino.
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Inference of Graphical Causal Models: Representing the Meaningful Information of Probability Distributions.. The Minimum Description Length Principle and Reasoning under Uncertainty, ILLC.