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MDL on the Web

Welcome to mdl-research.org. The purpose of statistical modeling is to discover regularities in observed data. The success in finding such regularities can be measured by the length with which the data can be described. This is the rationale behind the Minimum Description Length (MDL). Principle introduced by Jorma Rissanen. The data, i.e. to describe it using fewer symbols than needed to describe the data literally. ' (Grünwald, 1998). What is mdl-research.org? If you have any comments on this site plea...

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Welcome to mdl-research.org. The purpose of statistical modeling is to discover regularities in observed data. The success in finding such regularities can be measured by the length with which the data can be described. This is the rationale behind the Minimum Description Length (MDL). Principle introduced by Jorma Rissanen. The data, i.e. to describe it using fewer symbols than needed to describe the data literally. ' (Grünwald, 1998). What is mdl-research.org? If you have any comments on this site plea...

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MDL on the Web | mdl-research.net Reviews

https://mdl-research.net

Welcome to mdl-research.org. The purpose of statistical modeling is to discover regularities in observed data. The success in finding such regularities can be measured by the length with which the data can be described. This is the rationale behind the Minimum Description Length (MDL). Principle introduced by Jorma Rissanen. The data, i.e. to describe it using fewer symbols than needed to describe the data literally. ' (Grünwald, 1998). What is mdl-research.org? If you have any comments on this site plea...

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mdl-research.net

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MDL site: Related topics

http://www.mdl-research.net/related.html

Kolmogorov Complexity and Solomonoff Induction. Maintained by Marcus Hutter). The Kolmogorov Complexity mailing list. Compression based similarity analysis and learning. Information Theoretical Estimators (ITE) Toolbox. Entropy on the World Wide Web. IEEE Information Theory Society. Maintained by the Schneider Lab). Minimum Message Length (MML) Principle. Minimum Encoding Length Inference Page. Maintained by David Dowe. The Wikipedia MML page. A Brief History of MML. Message from Monte Carlo.

2

MDL site: On-line demonstrations

http://www.mdl-research.net/demo.html

Markov Chain Order Estimation. The feasibility of the MDL model selection criterion is illustrated by the following demonstration. We use the criterion for estimating the appropriate order of a Markov chain. Recall that a Markov chain is a process that generates symbols where the probability distribution of each symbol depends on the previous symbols only through the k. Immediate predecessors. The value k. The model(s) giving the shortest code-length to the typed sequence is shown in green.

3

MDL site: Reading

http://www.mdl-research.net/reading.html

There is a large body of literature on the Minimum Description Length principle in the contexts of statistics, mathematics, machine learning, philosophy, etc. We give only a small selection of publications that we have found especially useful and important. More publications can be easily found using search engines such as Google. ABarron, J.Rissanen, and B.Yu, The minimum description length principle in coding and modeling. IEEE Trans. Information Theory,. Vol 44 (1998), no. 6, pp. 2743-2760. PGrünwald,...

4

MDL site: People

http://www.mdl-research.net/people.html

Please e-mail teemu.roos@hiit.fi. In case you wish to be listed below (also provide a link to your homepage) or to be removed from the list. Stephen F. Bush. Prediction of individual sequences). Prediction of individual sequences). Information theory and statistics). Prediction of individual sequences). Prediction of individual sequences). Prediction of individual sequences). Also at Royal Holloway. Zoltán Szabó. Information theory, independent subspace analysis and its extensions; ITE Toolbox.

5

Jorma Rissanen

http://www.mdl-research.net/jorma.rissanen

Jorma Rissanen, Professor Emeritus. Development of information theory as the foundation of statistical modeling, above all theory of the MDL principle. And stochastic complexity. Extension of these. Coding theory for data compression. IEEE Trans. Information Theory, 46(7), pp. 2537-2543. Strong Optimality of the Normalized ML Models as Universal Codes and Information in Data. IEEE Trans. Information Theory, vol. 47(5), pp. 1712-1717. Complexity of Nonlogarithmic Loss Functions.

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Algorithmic Information Theory

http://www.hutter1.net/ait.htm

Future events, . Courses, Info on A.K. Legacy of Ray Solomonoff. Andrei Kolmogorov The theory of probability . can and should be developed from axioms . Entities should not be multiplied unnecessarily. 11th International Conference on Computability, Complexity and Randomness (CCR 2016). 2016, January 4-8, Honolulu, Hawaii, USA. Introduction / What is AIT? Algorithmic Information Theory (AIT). Algorithmic "Kolmogorov" Complexity (AC). Algorithmic "Solomonoff" Probability (AP). Universal "Levin" Search (US).

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mdl-research.net

MDL on the Web

Welcome to mdl-research.org. The purpose of statistical modeling is to discover regularities in observed data. The success in finding such regularities can be measured by the length with which the data can be described. This is the rationale behind the Minimum Description Length (MDL). Principle introduced by Jorma Rissanen. The data, i.e. to describe it using fewer symbols than needed to describe the data literally. ' (Grünwald, 1998). What is mdl-research.org? If you have any comments on this site plea...

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