Bayesian Networks: A Practical Guide to Applications

Bayesian Networks, the result of the convergence of artificial intelligence with statistics, are growing in popularity. Their versatility and modelling power is now employed across a variety of fields for the purposes of analysis, simulation, prediction and diagnosis.

This book provides a general introduction to Bayesian networks, defining and illustrating the basic concepts with pedagogical examples and twenty real-life case studies drawn from a range of fields including medicine, computing, natural sciences and engineering.

Designed to help analysts, engineers, scientists and professionals taking part in complex decision processes to successfully implement Bayesian networks, this book equips readers with proven methods to generate, calibrate, evaluate and validate Bayesian networks.

The book:

• Provides the tools to overcome common practical challenges such as the treatment of missing input data, interaction with experts and decision makers, determination of the optimal granularity and size of the model. 

• Highlights the strengths of Bayesian networks whilst also presenting a discussion of their limitations.

• Compares Bayesian networks with other modelling techniques such as neural networks, fuzzy logic and fault trees.

• Describes, for ease of comparison, the main features of the major Bayesian network software packages: Netica, Hugin, Elvira and Discoverer, from the point of view of the user.

• Offers a historical perspective on the subject and analyses future directions for research.

Written by leading experts with practical experience of applying Bayesian networks in finance, banking, medicine, robotics, civil engineering, geology, geography, genetics, forensic science, ecology, and industry, the book has much to offer both practitioners and researchers involved in statistical analysis or modelling in any of these fields.

貝葉斯網絡是人工智能與統計學相結合的結果，正越來越受到歡迎。它們的多功能性和建模能力現在被應用於各個領域，用於分析、模擬、預測和診斷。

本書提供了貝葉斯網絡的一般介紹，通過教學示例和二十個真實案例研究，從醫學、計算機、自然科學和工程等領域繪製，定義和說明了基本概念。

本書旨在幫助參與複雜決策過程的分析師、工程師、科學家和專業人士成功實施貝葉斯網絡，為讀者提供了生成、校準、評估和驗證貝葉斯網絡的成熟方法。

本書：
- 提供了克服常見實際挑戰的工具，例如處理缺失的輸入數據、與專家和決策者的互動、確定模型的最佳粒度和大小。
- 強調了貝葉斯網絡的優勢，同時討論了它們的局限性。
- 將貝葉斯網絡與其他建模技術（如神經網絡、模糊邏輯和故障樹）進行比較。
- 從用戶的角度描述了主要貝葉斯網絡軟件包（Netica、Hugin、Elvira和Discoverer）的主要特點，以便進行比較。
- 提供了該主題的歷史背景，並分析了未來的研究方向。

本書由在金融、銀行、醫學、機器人技術、土木工程、地質學、地理學、遺傳學、法醫科學、生態學和工業等領域應用貝葉斯網絡的領先專家撰寫，對於這些領域的統計分析或建模的從業人員和研究人員都有很大的價值。