Data Analysis: A Bayesian Tutorial, 2/e (Paperback)

Devinderjit Sivia, John Skilling

Data Analysis: A Bayesian Tutorial, 2/e (Paperback)

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Statistics lectures have been a source of much bewilderment and frustration for generations of students. This book attempts to remedy the situation by expounding a logical and unified approach to the whole subject of data analysis.

This text is intended as a tutorial guide for senior undergraduates and research students in science and engineering. After explaining the basic principles of Bayesian probability theory, their use is illustrated with a variety of examples ranging from elementary parameter estimation to image processing. Other topics covered include reliability analysis, multivariate optimization, least-squares and maximum likelihood, error-propagation, hypothesis testing, maximum entropy and experimental design.

 

 

 

Table of Contents

1. The Basics , Sivia
2. Parameter Estimation I , Sivia
3. Parameter Estimation II , Sivia
4. Model Selection , Sivia
5. Assigning Probabilities , Sivia
6. Non-parametric Estimation , Sivia
7. Experimental Design , Sivia
8. Least-Squares Extensions , Sivia
9. Nested Sampling , Skilling
10. Quantification , Skilling
Appendices
Bibliography

商品描述(中文翻譯)

描述


統計學講座一直以來都是許多學生困惑和挫折的來源。本書試圖通過闡述一種邏輯和統一的方法來解決這個問題，涵蓋整個數據分析的主題。



本書旨在成為科學和工程領域的高年級本科生和研究生的教程指南。在解釋了貝葉斯概率理論的基本原理之後，通過各種例子來說明其應用，從基本參數估計到圖像處理。其他涵蓋的主題包括可靠性分析、多變量優化、最小二乘法和最大似然估計、誤差傳播、假設檢驗、最大熵和實驗設計。

 


 

 


目錄





1. 基礎知識 , Sivia

2. 參數估計 I , Sivia

3. 參數估計 II , Sivia

4. 模型選擇 , Sivia

5. 分配概率 , Sivia

6. 非參數估計 , Sivia

7. 實驗設計 , Sivia

8. 最小二乘法擴展 , Sivia

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