| 書目名稱 | Universal Coding and Order Identification by Model Selection Methods |
| 編輯 | élisabeth Gassiat |
| 視頻video | http://file.papertrans.cn/943/942212/942212.mp4 |
| 概述 | Offers a comprehensive introduction to coding theory: the reader does not need a lot of background.Illustrates links between coding theory and statistical inference.Presents applications to order iden |
| 叢書名稱 | Springer Monographs in Mathematics |
| 圖書封面 |  |
| 描述 | The purpose of these notes is to highlight the far-reaching connections between Information Theory and Statistics. Universal coding and adaptive compression are indeed closely related to statistical inference concerning processes and using maximum likelihood or Bayesian methods. The book is divided into four chapters, the first of which introduces readers to lossless coding, provides an intrinsic lower bound on the codeword length in terms of Shannon’s entropy, and presents some coding methods that can achieve this lower bound, provided the source distribution is known. In turn, Chapter 2 addresses universal coding on finite alphabets, and seeks to find coding procedures that can achieve the optimal compression rate, regardless of the source distribution. It also quantifies the speed of convergence of the compression rate to the source entropy rate. These powerful results do not extend to infinite alphabets. In Chapter 3, it is shown that there are no universal codes over the class of stationary ergodic sources over a countable alphabet. This negative result prompts at least two different approaches: the introduction of smaller sub-classes of sources known as envelope classes, over |
| 出版日期 | Book 2018 |
| 關(guān)鍵詞 | 68P30, 62C10; Universal Coding; Adaptive Compression; Hidden Markov Chains; Model Selection; Infinite Alp |
| 版次 | 1 |
| doi | https://doi.org/10.1007/978-3-319-96262-7 |
| isbn_softcover | 978-3-030-07167-7 |
| isbn_ebook | 978-3-319-96262-7Series ISSN 1439-7382 Series E-ISSN 2196-9922 |
| issn_series | 1439-7382 |
| copyright | Springer International Publishing AG, part of Springer Nature 2018 |
|Archiver|手機(jī)版|小黑屋|
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