Data Analysis : A Bayesian Tutorial

D. S. Sivia

ISBN 10: 0198568320 / ISBN 13: 9780198568322

Published by OUP UK, 2006

About this Item

nach der Bestellung gedruckt Neuware - Printed after ordering - 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 imageprocessing. 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 techniquefor Bayesian computation called 'nested sampling'.

Seller Inventory # 9780198568322

Bibliographic Details

• Title: Data Analysis : A Bayesian Tutorial
• Publisher: OUP UK
• Publication Date: 2006
• Language: English
• ISBN 10: 0198568320
• ISBN 13: 9780198568322
• Binding: Taschenbuch
• Edition: 2nd Edition
• Condition: Neu
• Dimensions: 234W x 156H x 15D millimeters 234 millimeters width by 156 millimeters height by 15 millimeters depth
• Weight: 401 grams

About this title

Synopsis

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'.

About the Author

Devinderjit Singh Sivia
Rutherford Appleton Laboratory
Chilton
Oxon
OX11 5DJ John Skilling
Maximum Entropy Data Consultants
42 Southgate Street
Bury St Edmonds
Suffolk
IP33 2AZ