Data analysis : a Bayesian tutorial
著者
書誌事項
Data analysis : a Bayesian tutorial
（Oxford science publications）
Oxford University Press, 2006
2nd ed
- : pbk
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注記
Previous ed.: 1996
Includes bibliographical references (p. [237]-240) and index
内容説明・目次
内容説明
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.
The Second Edition of this successful tutorial book contains a new chapter on extensions to the ubiquitous least-squares procedure, allowing for the straightforward handling of outliers and unknown correlated noise, and a cutting-edge contribution from John Skilling on a novel numerical technique for Bayesian computation called 'nested sampling'.
目次
- 1. The Basics
- 2. Parameter Estimation I
- 3. Parameter Estimation II
- 4. Model Selection
- 5. Assigning Probabilities
- 6. Non-parametric Estimation
- 7. Experimental Design
- 8. Least-Squares Extensions
- 9. Nested Sampling
- 10. Quantification
- Appendices
- Bibliography
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