An Introduction to Kolmogorov Complexity and Its Applications (Texts in Computer Science)
暫譯: 科爾莫哥洛夫複雜性及其應用導論(計算機科學文本)

Ming Li, Paul M.B. Vitányi

  • 出版商: Springer
  • 出版日期: 2008-11-21
  • 售價: $4,200
  • 貴賓價: 9.5$3,990
  • 語言: 英文
  • 頁數: 792
  • 裝訂: Hardcover
  • ISBN: 0387339981
  • ISBN-13: 9780387339986
  • 相關分類: Computer-Science
  • 海外代購書籍(需單獨結帳)

相關主題

商品描述

“The book is outstanding and admirable in many respects. ... is necessary reading for all kinds of readers from undergraduate students to top authorities in the field.” Journal of Symbolic Logic

Written by two experts in the field, this is the only comprehensive and unified treatment of the central ideas and applications of Kolmogorov complexity. The book presents a thorough treatment of the subject with a wide range of illustrative applications. Such applications include the randomness of finite objects or infinite sequences, Martin-Loef tests for randomness, information theory, computational learning theory, the complexity of algorithms, and the thermodynamics of computing. It will be ideal for advanced undergraduate students, graduate students, and researchers in computer science, mathematics, cognitive sciences, philosophy, artificial intelligence, statistics, and physics. The book is self-contained in that it contains the basic requirements from mathematics and computer science. Included are also numerous problem sets, comments, source references, and hints to solutions of problems. New topics in this edition include Omega numbers, Kolmogorov–Loveland randomness, universal learning, communication complexity, Kolmogorov's random graphs, time-limited universal distribution, Shannon information and others.

商品描述(中文翻譯)

「這本書在許多方面都非常出色且令人欽佩。……對於各類讀者,從本科生到該領域的頂尖權威,都是必讀之作。」——《符號邏輯期刊》

由兩位該領域的專家撰寫,這是對Kolmogorov複雜性中心思想及其應用的唯一全面且統一的探討。這本書對該主題進行了徹底的闡述,並提供了廣泛的示例應用。這些應用包括有限物體或無限序列的隨機性、Martin-Loef隨機性測試、信息理論、計算學習理論、算法的複雜性以及計算的熱力學。這本書非常適合高年級本科生、研究生以及計算機科學、數學、認知科學、哲學、人工智慧、統計學和物理學的研究人員。該書是自足的,包含了數學和計算機科學的基本要求。此外,書中還包含了大量的習題集、評論、參考資料和問題解答提示。本版新增的主題包括Omega數、Kolmogorov–Loveland隨機性、通用學習、通信複雜性、Kolmogorov的隨機圖、時間限制的通用分佈、Shannon信息等。