BAYESIAN METHODS: AN ANALYSIS FOR STATISTICIANS AND INTERDISCIPLINARY RESEARCHERS

Leonard, Thomas & Hsu, John S.J.

ISBN 10: 0521004144 / ISBN 13: 9780521004145

Edité par Cambridge University Press, 2001

A propos de cet article

2001. Almost very good condition. Cambridge Series in Statistical and Probabilistic Mathematics. Contains numerous worked examples, self-study exercises, and practical applications. Essential reading for final-year undergraduates, Masters-degree and graduate students, statisticians, and other interdisciplinary researchers wishing to develop good-quality conclusions from their data and to pursue the notion of scientific truth. Blue cardwraps. Reprint. Cover edges rubbed and worn, tiny split to front spine edge. A few light creases and marks to covers. Vertical edge of text block stained. Front hinge pulled but binding tight. Possibly some infrequent annotation not noticed on first inspection. Packaged with care and promptly dispatched! N° de réf. du vendeur 1821085

Détails bibliographiques

Titre : BAYESIAN METHODS: AN ANALYSIS FOR ...

Éditeur : Cambridge University Press

Date d'édition : 2001

Reliure : Softcover

Etat : Very Good

Etat de la jaquette : No Jacket

A propos de ce titre

Synopsis :

Book by Leonard Thomas Hsu John S J

Présentation de l'éditeur:

This book describes the Bayesian approach to statistics at a level suitable for final year undergraduate and Masters students. It is unusual in presenting Bayesian statistics with a practical flavor and an emphasis on mainstream statistics, showing how to infer scientific, medical, and social conclusions from numerical data. The authors draw on many years of experience with practical and research programs and describe many statistical methods, not readily available elsewhere. A first chapter on Fisherian methods, together with a strong overall emphasis on likelihood, makes the text suitable for mainstream statistics courses whose instructors wish to follow mixed or comparative philosophies. The other chapters contain important sections relating to many areas of statistics such as the linear model, categorical data analysis, time series and forecasting, mixture models, survival analysis, Bayesian smoothing, and non-linear random effects models. The text includes a large number of practical examples, worked examples, and exercises. It will be essential reading for all statisticians, statistics students, and related interdisciplinary researchers.