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Record Nr. |
UNINA9910777833803321 |
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Autore |
Grünwald Peter D |
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Titolo |
The minimum description length principle / / Peter D. Grünwald |
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Pubbl/distr/stampa |
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Cambridge, Mass., : MIT Press, ©2007 |
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ISBN |
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1-282-09635-4 |
9786612096358 |
0-262-25629-0 |
1-4294-6560-3 |
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Descrizione fisica |
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1 online resource (736 p.) |
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Collana |
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Adaptive computation and machine learning |
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Disciplina |
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Soggetti |
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Minimum description length (Information theory) |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Note generali |
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Description based upon print version of record. |
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Nota di bibliografia |
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Includes bibliographical references (p. [651]-673) and indexes. |
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Nota di contenuto |
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Contents; List of Figures; Series Foreword; Foreword; Preface; PART I - Introductory Material; 1 - Learning, Regularity, and Compression; 2 - Probabilistic and Statistical Preliminaries; 3 - Information-Theoretic Preliminaries; 4 - Information-Theoretic Properties of Statistical Models; 5 - Crude Two-Part Code MDL; PART II - Universal Coding; 6 - Universal Coding with Countable Models; 7 - Parametric Models: Normalized Maximum Likelihood; 8 - Parametric Models: Bayes; 9 - Parametric Models: Prequential Plug-in; 10 - Parametric Models: Two-Part; 11 - NMLWith Innite Complexity |
12 - Linear RegressionPART III - Refined MDL; 14 - MDL Model Selection; 15 - MDL Prediction and Estimation; 16 - MDL Consistency and Convergence; 17 - MDL in Context; PART IV - Additional Background; 18 - The Exponential or "Maximum Entropy" Families; 19 - Information-Theoretic Properties of Exponential Families; References; List of Symbols; Subject Index |
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Sommario/riassunto |
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A comprehensive introduction and reference guide to the minimum description length (MDL) Principle that is accessible to researchers dealing with inductive reference in diverse areas including statistics, pattern classification, machine learning, data min. |
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