Data Analysis : A Bayesian Tutorial

D. S. Sivia

ISBN 10: 0198568320 / ISBN 13: 9780198568322

Edité par OUP UK, 2006

A propos de cet article

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'.

N° de réf. du vendeur 9780198568322

Détails bibliographiques

• Titre: Data Analysis : A Bayesian Tutorial
• Éditeur: OUP UK
• Date de parution: 2006
• Langue: anglais
• ISBN à 10 chiffres: 0198568320
• ISBN à 13 chiffres: 9780198568322
• Reliure: Taschenbuch
• Édition: 2ème Édition
• État de l'article: Neu

A propos de ce titre

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'.

À propos de l?auteur

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