Available:*
Library | Item Barcode | Call Number | Material Type | Item Category 1 | Status |
---|---|---|---|---|---|
Searching... | 30000010205309 | QA279.5 S58 2006 | Open Access Book | Book | Searching... |
On Order
Summary
Summary
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'.
Author Notes
Devinderjit Singh SiviaRutherford Appleton LaboratoryChiltonOxonOX11 5DJJohn SkillingMaximum Entropy Data Consultants42 Southgate StreetBury St EdmondsSuffolkIP33 2AZ
Table of Contents
1 Sivia: The Basics |
2 Sivia: Parameter Estimation I |
3 Sivia: Parameter Estimation II |
4 Sivia: Model Selection |
5 Sivia: Assigning Probabilities |
6 Sivia: Non-parametric Estimation |
7 Sivia: Experimental Design |
8 Sivia: Least-Squares Extensions |
9 Skilling: Nested Sampling |
10 Skilling: Quantification |
Appendices |
Bibliography |