Description
Book SynopsisStatistics 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 o
Trade ReviewOne 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 *
Table of Contents1. 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
