The Minimum Description Length Principle
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Author |
: Peter D. Grünwald |
Publisher |
: MIT Press |
Total Pages |
: 736 |
Release |
: 2007 |
ISBN-10 |
: 9780262072816 |
ISBN-13 |
: 0262072815 |
Rating |
: 4/5 (16 Downloads) |
Synopsis The Minimum Description Length Principle by : Peter D. Grünwald
This introduction to the MDL Principle provides a reference accessible to graduate students and researchers in statistics, pattern classification, machine learning, and data mining, to philosophers interested in the foundations of statistics, and to researchers in other applied sciences that involve model selection.
Author |
: Peter D. Grünwald |
Publisher |
: MIT Press |
Total Pages |
: 464 |
Release |
: 2005 |
ISBN-10 |
: 0262072629 |
ISBN-13 |
: 9780262072625 |
Rating |
: 4/5 (29 Downloads) |
Synopsis Advances in Minimum Description Length by : Peter D. Grünwald
A source book for state-of-the-art MDL, including an extensive tutorial and recent theoretical advances and practical applications in fields ranging from bioinformatics to psychology.
Author |
: Jorma Rissanen |
Publisher |
: Springer Science & Business Media |
Total Pages |
: 145 |
Release |
: 2007-12-15 |
ISBN-10 |
: 9780387688121 |
ISBN-13 |
: 0387688129 |
Rating |
: 4/5 (21 Downloads) |
Synopsis Information and Complexity in Statistical Modeling by : Jorma Rissanen
No statistical model is "true" or "false," "right" or "wrong"; the models just have varying performance, which can be assessed. The main theme in this book is to teach modeling based on the principle that the objective is to extract the information from data that can be learned with suggested classes of probability models. The intuitive and fundamental concepts of complexity, learnable information, and noise are formalized, which provides a firm information theoretic foundation for statistical modeling. Although the prerequisites include only basic probability calculus and statistics, a moderate level of mathematical proficiency would be beneficial.
Author |
: Imre Csiszár |
Publisher |
: Now Publishers Inc |
Total Pages |
: 128 |
Release |
: 2004 |
ISBN-10 |
: 1933019050 |
ISBN-13 |
: 9781933019055 |
Rating |
: 4/5 (50 Downloads) |
Synopsis Information Theory and Statistics by : Imre Csiszár
Information Theory and Statistics: A Tutorial is concerned with applications of information theory concepts in statistics, in the finite alphabet setting. The topics covered include large deviations, hypothesis testing, maximum likelihood estimation in exponential families, analysis of contingency tables, and iterative algorithms with an "information geometry" background. Also, an introduction is provided to the theory of universal coding, and to statistical inference via the minimum description length principle motivated by that theory. The tutorial does not assume the reader has an in-depth knowledge of Information Theory or statistics. As such, Information Theory and Statistics: A Tutorial, is an excellent introductory text to this highly-important topic in mathematics, computer science and electrical engineering. It provides both students and researchers with an invaluable resource to quickly get up to speed in the field.
Author |
: Jorma Rissanen |
Publisher |
: World Scientific |
Total Pages |
: 191 |
Release |
: 1998-10-07 |
ISBN-10 |
: 9789814507400 |
ISBN-13 |
: 9814507407 |
Rating |
: 4/5 (00 Downloads) |
Synopsis Stochastic Complexity In Statistical Inquiry by : Jorma Rissanen
This book describes how model selection and statistical inference can be founded on the shortest code length for the observed data, called the stochastic complexity. This generalization of the algorithmic complexity not only offers an objective view of statistics, where no prejudiced assumptions of 'true' data generating distributions are needed, but it also in one stroke leads to calculable expressions in a range of situations of practical interest and links very closely with mainstream statistical theory. The search for the smallest stochastic complexity extends the classical maximum likelihood technique to a new global one, in which models can be compared regardless of their numbers of parameters. The result is a natural and far reaching extension of the traditional theory of estimation, where the Fisher information is replaced by the stochastic complexity and the Cramer-Rao inequality by an extension of the Shannon-Kullback inequality. Ideas are illustrated with applications from parametric and non-parametric regression, density and spectrum estimation, time series, hypothesis testing, contingency tables, and data compression.
Author |
: Michael R. Berthold |
Publisher |
: Springer |
Total Pages |
: 588 |
Release |
: 2020-04-02 |
ISBN-10 |
: 3030445836 |
ISBN-13 |
: 9783030445836 |
Rating |
: 4/5 (36 Downloads) |
Synopsis Advances in Intelligent Data Analysis XVIII by : Michael R. Berthold
This open access book constitutes the proceedings of the 18th International Conference on Intelligent Data Analysis, IDA 2020, held in Konstanz, Germany, in April 2020. The 45 full papers presented in this volume were carefully reviewed and selected from 114 submissions. Advancing Intelligent Data Analysis requires novel, potentially game-changing ideas. IDA’s mission is to promote ideas over performance: a solid motivation can be as convincing as exhaustive empirical evaluation.
Author |
: Shai Shalev-Shwartz |
Publisher |
: Cambridge University Press |
Total Pages |
: 415 |
Release |
: 2014-05-19 |
ISBN-10 |
: 9781107057135 |
ISBN-13 |
: 1107057132 |
Rating |
: 4/5 (35 Downloads) |
Synopsis Understanding Machine Learning by : Shai Shalev-Shwartz
Introduces machine learning and its algorithmic paradigms, explaining the principles behind automated learning approaches and the considerations underlying their usage.
Author |
: Jorma Rissanen |
Publisher |
: Cambridge University Press |
Total Pages |
: 171 |
Release |
: 2012-06-07 |
ISBN-10 |
: 9781107004740 |
ISBN-13 |
: 1107004748 |
Rating |
: 4/5 (40 Downloads) |
Synopsis Optimal Estimation of Parameters by : Jorma Rissanen
A comprehensive and consistent theory of estimation, including a description of a powerful new tool, the generalized maximum capacity estimator.
Author |
: Jonas Peters |
Publisher |
: MIT Press |
Total Pages |
: 289 |
Release |
: 2017-11-29 |
ISBN-10 |
: 9780262037310 |
ISBN-13 |
: 0262037319 |
Rating |
: 4/5 (10 Downloads) |
Synopsis Elements of Causal Inference by : Jonas Peters
A concise and self-contained introduction to causal inference, increasingly important in data science and machine learning. The mathematization of causality is a relatively recent development, and has become increasingly important in data science and machine learning. This book offers a self-contained and concise introduction to causal models and how to learn them from data. After explaining the need for causal models and discussing some of the principles underlying causal inference, the book teaches readers how to use causal models: how to compute intervention distributions, how to infer causal models from observational and interventional data, and how causal ideas could be exploited for classical machine learning problems. All of these topics are discussed first in terms of two variables and then in the more general multivariate case. The bivariate case turns out to be a particularly hard problem for causal learning because there are no conditional independences as used by classical methods for solving multivariate cases. The authors consider analyzing statistical asymmetries between cause and effect to be highly instructive, and they report on their decade of intensive research into this problem. The book is accessible to readers with a background in machine learning or statistics, and can be used in graduate courses or as a reference for researchers. The text includes code snippets that can be copied and pasted, exercises, and an appendix with a summary of the most important technical concepts.
Author |
: Vladimir Vapnik |
Publisher |
: Springer Science & Business Media |
Total Pages |
: 324 |
Release |
: 2013-06-29 |
ISBN-10 |
: 9781475732641 |
ISBN-13 |
: 1475732643 |
Rating |
: 4/5 (41 Downloads) |
Synopsis The Nature of Statistical Learning Theory by : Vladimir Vapnik
The aim of this book is to discuss the fundamental ideas which lie behind the statistical theory of learning and generalization. It considers learning as a general problem of function estimation based on empirical data. Omitting proofs and technical details, the author concentrates on discussing the main results of learning theory and their connections to fundamental problems in statistics. This second edition contains three new chapters devoted to further development of the learning theory and SVM techniques. Written in a readable and concise style, the book is intended for statisticians, mathematicians, physicists, and computer scientists.