Bayesian Networks: An Introduction (Hardcover)

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.

《Bayesian Networks: An Introduction》提供了一個自成一體的介紹，關於貝葉斯網絡的理論和應用，這是統計學家、計算機科學家和從事建模複雜數據集的人感興趣且重要的主題。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概率、統計和數學知識。所有概念都被仔細解釋，並且包含練習題。

特點包括:
- 介紹了狄利克雷分布、指數族及其應用。
- 詳細描述了學習算法和使用聯合樹方法的條件高斯分布。
- 討論了Pearl的干預計算，並介紹了看和做條件的概念。
- 所有概念都有清晰的定義，並通過例子和練習題進行說明。解答可以在線上找到。

這本書將成為統計學、計算機工程、數學、數據挖掘、人工智能和生物學等研究生學生的寶貴資源。同樣，對於使用類似建模或統計技術如神經網絡的研究人員和用戶，這本書也會很有興趣。