Information theory and the central limit theorem:
Saved in:
| Main Author: | |
|---|---|
| Format: | Electronic eBook |
| Language: | English |
| Published: |
London
Imperial College Press
©2004
|
| Subjects: | |
| Online Access: | FAW01 FAW02 Volltext |
| Item Description: | Includes bibliographical references (pages 199-206) and index Information Theory and The Central Limit Theorem; Preface; Contents; 1. Introduction to Information Theory; 2. Convergence in Relative Entropy; 3. Non-Identical Variables and Random Vectors; 4. Dependent Random Variables; 5. Convergence to Stable Laws; 6. Convergence on Compact Groups; 7. Convergence to the Poisson Distribution; 8. Free Random Variables; Appendix A Calculating Entropies; Appendix B Poincare Inequalities; Appendix C de Bruijn Identity; Appendix D Entropy Power Inequality; Appendix E Relationships Between Different Forms of Convergence; Bibliography; Index This book provides a comprehensive description of a new method of proving the central limit theorem, through the use of apparently unrelated results from information theory. It gives a basic introduction to the concepts of entropy and Fisher information, and collects together standard results concerning their behaviour. It brings together results from a number of research papers as well as unpublished material, showing how the techniques can give a unified view of limit theorems |
| Physical Description: | 1 Online-Ressource (xiv, 209 pages) |
| ISBN: | 1860944736 1860945376 9781860944734 9781860945373 |
Staff View
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| author | Johnson, Oliver, (Oliver Thomas) |
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| id | DE-604.BV043158287 |
| illustrated | Not Illustrated |
| indexdate | 2024-07-10T07:19:16Z |
| institution | BVB |
| isbn | 1860944736 1860945376 9781860944734 9781860945373 |
| language | English |
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| physical | 1 Online-Ressource (xiv, 209 pages) |
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| publishDate | 2004 |
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| spelling | Johnson, Oliver, (Oliver Thomas) Verfasser aut Information theory and the central limit theorem Oliver Johnson London Imperial College Press ©2004 1 Online-Ressource (xiv, 209 pages) txt rdacontent c rdamedia cr rdacarrier Includes bibliographical references (pages 199-206) and index Information Theory and The Central Limit Theorem; Preface; Contents; 1. Introduction to Information Theory; 2. Convergence in Relative Entropy; 3. Non-Identical Variables and Random Vectors; 4. Dependent Random Variables; 5. Convergence to Stable Laws; 6. Convergence on Compact Groups; 7. Convergence to the Poisson Distribution; 8. Free Random Variables; Appendix A Calculating Entropies; Appendix B Poincare Inequalities; Appendix C de Bruijn Identity; Appendix D Entropy Power Inequality; Appendix E Relationships Between Different Forms of Convergence; Bibliography; Index This book provides a comprehensive description of a new method of proving the central limit theorem, through the use of apparently unrelated results from information theory. It gives a basic introduction to the concepts of entropy and Fisher information, and collects together standard results concerning their behaviour. It brings together results from a number of research papers as well as unpublished material, showing how the techniques can give a unified view of limit theorems MATHEMATICS / Probability & Statistics / General bisacsh Central limit theorem fast Information theory / Statistical methods fast Probabilities fast Central limit theorem Information theory Statistical methods Probabilities Zufallsvariable (DE-588)4129514-6 gnd rswk-swf Informationstheorie (DE-588)4026927-9 gnd rswk-swf Zufallsvektor (DE-588)4191098-9 gnd rswk-swf Zufallsvariable (DE-588)4129514-6 s 1\p DE-604 Informationstheorie (DE-588)4026927-9 s 2\p DE-604 Zufallsvektor (DE-588)4191098-9 s 3\p DE-604 http://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&db=nlabk&AN=130010 Aggregator Volltext 1\p cgwrk 20201028 DE-101 https://d-nb.info/provenance/plan#cgwrk 2\p cgwrk 20201028 DE-101 https://d-nb.info/provenance/plan#cgwrk 3\p cgwrk 20201028 DE-101 https://d-nb.info/provenance/plan#cgwrk |
| spellingShingle | Johnson, Oliver, (Oliver Thomas) Information theory and the central limit theorem MATHEMATICS / Probability & Statistics / General bisacsh Central limit theorem fast Information theory / Statistical methods fast Probabilities fast Central limit theorem Information theory Statistical methods Probabilities Zufallsvariable (DE-588)4129514-6 gnd Informationstheorie (DE-588)4026927-9 gnd Zufallsvektor (DE-588)4191098-9 gnd |
| subject_GND | (DE-588)4129514-6 (DE-588)4026927-9 (DE-588)4191098-9 |
| title | Information theory and the central limit theorem |
| title_auth | Information theory and the central limit theorem |
| title_exact_search | Information theory and the central limit theorem |
| title_full | Information theory and the central limit theorem Oliver Johnson |
| title_fullStr | Information theory and the central limit theorem Oliver Johnson |
| title_full_unstemmed | Information theory and the central limit theorem Oliver Johnson |
| title_short | Information theory and the central limit theorem |
| title_sort | information theory and the central limit theorem |
| topic | MATHEMATICS / Probability & Statistics / General bisacsh Central limit theorem fast Information theory / Statistical methods fast Probabilities fast Central limit theorem Information theory Statistical methods Probabilities Zufallsvariable (DE-588)4129514-6 gnd Informationstheorie (DE-588)4026927-9 gnd Zufallsvektor (DE-588)4191098-9 gnd |
| topic_facet | MATHEMATICS / Probability & Statistics / General Central limit theorem Information theory / Statistical methods Probabilities Information theory Statistical methods Zufallsvariable Informationstheorie Zufallsvektor |
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