256 pages, b/w line drawings
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 optimisation, 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'.
One of the strengths of this book is the author's ability to motivate the use of Bayesian methods through simple yet effective examples. Katie St. Clair MAA Reviews
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
There are currently no reviews for this product. Be the first to review this product!