Journal ArticleDOI
Hypothesis Selection and Testing by the MDL Principle
TLDR
The central idea of the MDL (Minimum Description Length) principle is to represent a class of models (hypotheses) by a universal model capable of imitating the behavior of any model in the class.Abstract:
The central idea of the MDL (Minimum Description Length) principle is to represent a class of models (hypotheses) by a universal model capable of imitating the behavior of any model in the class. The principle calls for a model class whose representative assigns the largest probability or density to the observed data. Two examples of universal models for parametric classes M are the normalized maximum likelihood (NML) modelread more
Citations
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Journal ArticleDOI
The minimum description length principle in coding and modeling
TL;DR: The normalized maximized likelihood, mixture, and predictive codings are each shown to achieve the stochastic complexity to within asymptotically vanishing terms.
Journal ArticleDOI
Graph based anomaly detection and description: a survey
TL;DR: This survey aims to provide a general, comprehensive, and structured overview of the state-of-the-art methods for anomaly detection in data represented as graphs, and gives a general framework for the algorithms categorized under various settings.
Posted Content
Graph-based Anomaly Detection and Description: A Survey
TL;DR: A comprehensive survey of the state-of-the-art methods for anomaly detection in data represented as graphs can be found in this article, where the authors highlight the effectiveness, scalability, generality, and robustness aspects of the methods.
Journal ArticleDOI
Toward a method of selecting among computational models of cognition.
TL;DR: A method of selecting among mathematical models of cognition known as minimum description length is introduced, which provides an intuitive and theoretically well-grounded understanding of why one model should be chosen.
Book
Advances in Minimum Description Length: Theory and Applications (Neural Information Processing)
TL;DR: Advances in Minimum Description Length is a sourcebook that will introduce the scientific community to the foundations of MDL, recent theoretical advances, and practical applications, and examples of how to apply MDL in research settings that range from bioinformatics and machine learning to psychology.
References
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Book
Elements of information theory
Thomas M. Cover,Joy A. Thomas +1 more
TL;DR: The author examines the role of entropy, inequality, and randomness in the design of codes and the construction of codes in the rapidly changing environment.
Journal ArticleDOI
Stastical Decision Theory and Bayesian Analysis.
Malay Ghosh,James O. Berger +1 more
Journal ArticleDOI
Paper: Modeling by shortest data description
TL;DR: The number of digits it takes to write down an observed sequence x1,...,xN of a time series depends on the model with its parameters that one assumes to have generated the observed data.
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Statistical Decision Theory and Bayesian Analysis
TL;DR: An overview of statistical decision theory, which emphasizes the use and application of the philosophical ideas and mathematical structure of decision theory.
Book
An Introduction to Kolmogorov Complexity and Its Applications
Ming Li,Paul M. B. Vitányi +1 more
TL;DR: The Journal of Symbolic Logic as discussed by the authors presents a thorough treatment of the subject with a wide range of illustrative applications such as the randomness of finite objects or infinite sequences, Martin-Loef tests for randomness, information theory, computational learning theory, the complexity of algorithms, and the thermodynamics of computing.