Bayesian Networks: An Introduction (Hardcover)
暫譯: 貝葉斯網路：入門（精裝版）

Timo Koski, John Noble

Bayesian Networks: An Introduction (Hardcover)

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商品描述

Bayesian Networks: An Introduction provides a self-contained introduction to the theory and applications of Bayesian networks, a topic of interest and importance for statisticians, computer scientists and those involved in modelling complex data sets. The material has been extensively tested in classroom teaching and assumes a basic knowledge of probability, statistics and mathematics. All notions are carefully explained and feature exercises throughout.

Features include:

  • An introduction to Dirichlet Distribution, Exponential Families and their applications.
  • A detailed description of learning algorithms and Conditional Gaussian Distributions using Junction Tree methods.
  • A discussion of Pearl's intervention calculus, with an introduction to the notion of see and do conditioning.
  • All concepts are clearly defined and illustrated with examples and exercises. Solutions are provided online.

This book will prove a valuable resource for postgraduate students of statistics, computer engineering, mathematics, data mining, artificial intelligence, and biology.

Researchers and users of comparable modelling or statistical techniques such as neural networks will also find this book of interest.

商品描述(中文翻譯)

《貝葉斯網路：入門》提供了一個自成體系的貝葉斯網路理論與應用的介紹，這是一個對統計學家、計算機科學家以及從事複雜數據集建模的人士來說都非常重要的主題。這些材料在課堂教學中經過廣泛測試，假設讀者具備基本的機率、統計和數學知識。所有概念都經過仔細解釋，並在全書中包含練習題。

本書的特色包括：
- 對狄利克雷分佈（Dirichlet Distribution）、指數族（Exponential Families）及其應用的介紹。
- 使用聯合樹（Junction Tree）方法對學習算法和條件高斯分佈（Conditional Gaussian Distributions）的詳細描述。
- 討論Pearl的介入微積分（intervention calculus），並介紹「看見與行動條件」（see and do conditioning）的概念。
- 所有概念都清楚定義，並以範例和練習題進行說明。解答可在線獲得。

本書將成為統計學、計算機工程、數學、數據挖掘、人工智慧和生物學研究生的寶貴資源。

從事類似建模或統計技術（如神經網路）的研究人員和使用者也會對本書感興趣。

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