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The minimum description length principle / / Peter D. Grünwald
| The minimum description length principle / / Peter D. Grünwald |
| Autore | Grünwald Peter D |
| Pubbl/distr/stampa | Cambridge, Mass., : MIT Press, ©2007 |
| Descrizione fisica | 1 online resource (736 p.) |
| Disciplina | 003/.54 |
| Collana | Adaptive computation and machine learning |
| Soggetto topico | Minimum description length (Information theory) |
| Soggetto non controllato | COMPUTER SCIENCE/Machine Learning & Neural Networks |
| ISBN |
1-282-09635-4
9786612096358 0-262-25629-0 1-4294-6560-3 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
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 |
| Record Nr. | UNINA-9910777833803321 |
Grünwald Peter D
|
||
| Cambridge, Mass., : MIT Press, ©2007 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
The minimum description length principle / / Peter D. Grünwald
| The minimum description length principle / / Peter D. Grünwald |
| Autore | Grünwald Peter D |
| Pubbl/distr/stampa | Cambridge, Mass., : MIT Press, ©2007 |
| Descrizione fisica | 1 online resource (736 p.) |
| Disciplina | 003/.54 |
| Collana | Adaptive computation and machine learning |
| Soggetto topico | Minimum description length (Information theory) |
| Soggetto non controllato | COMPUTER SCIENCE/Machine Learning & Neural Networks |
| ISBN |
1-282-09635-4
9786612096358 0-262-25629-0 1-4294-6560-3 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
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 |
| Record Nr. | UNINA-9910812869503321 |
|
Grünwald Peter D
|
||
| Cambridge, Mass., : MIT Press, ©2007 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||