Articles liés à Bayes and Empirical Bayes Methods for Data Analysis

Bayes and Empirical Bayes Methods for Data Analysis - Couverture rigide

Carlin, Bradley P.; Louis, Thomas A.

 
9780412056116: Bayes and Empirical Bayes Methods for Data Analysis
Couverture rigide
ISBN 10 : 0412056119 ISBN 13 : 9780412056116
Editeur : Chapman & Hall/CRC, 1996

Synopsis

Recent advances in computing-leading to the ability to evaluate increasingly complex models-has resulted in a growing popularity of Bayes and empirical Bayes (EB) methods in statistical practice. Bayes and Empirical Bayes Methods for Data Analysis answers the need for a ready reference that can be read and appreciated by practicing statisticians as well as graduate students. It introduces Bayes and EB methods, demonstrates their usefulness in challenging applied settings, and shows how they can be implemented using modern Markov chain Monte Carlo (MCMC) computational methods. Avoiding philosophical nit-picking, it shows how properly structured Bayes and EB procedures have good frequentist and Bayesian performance both in theory and practice.
The authors have chosen a very practical focus for their work, offering real solution methods to researchers with challenging problems. Beginning with an outline of the decision-theoretic tools needed to compare procedures, the book presents the basics of Bayes and EB approaches. The authors evaluate the frequentist and empirical Bayes performance of these approaches in a variety of settings and identify both virtues and drawbacks. The second half of the book stresses applications. If offers an extensive discussion of modern Bayesian computation methods-including the Gibbs sampler and the Metropolis-Hastings algorithm. It describes data analytic tasks, and offers guidelines on using a variety of special methods and models. The authors conclude with three fully worked case studies of real data sets.

Les informations fournies dans la section « Synopsis » peuvent faire référence à une autre édition de ce titre.

Présentation de l'éditeur

Recent advances in computing-leading to the ability to evaluate increasingly complex models-has resulted in a growing popularity of Bayes and empirical Bayes (EB) methods in statistical practice. Bayes and Empirical Bayes Methods for Data Analysis answers the need for a ready reference that can be read and appreciated by practicing statisticians as well as graduate students. It introduces Bayes and EB methods, demonstrates their usefulness in challenging applied settings, and shows how they can be implemented using modern Markov chain Monte Carlo (MCMC) computational methods. Avoiding philosophical nit-picking, it shows how properly structured Bayes and EB procedures have good frequentist and Bayesian performance both in theory and practice.
The authors have chosen a very practical focus for their work, offering real solution methods to researchers with challenging problems. Beginning with an outline of the decision-theoretic tools needed to compare procedures, the book presents the basics of Bayes and EB approaches. The authors evaluate the frequentist and empirical Bayes performance of these approaches in a variety of settings and identify both virtues and drawbacks. The second half of the book stresses applications. If offers an extensive discussion of modern Bayesian computation methods-including the Gibbs sampler and the Metropolis-Hastings algorithm. It describes data analytic tasks, and offers guidelines on using a variety of special methods and models. The authors conclude with three fully worked case studies of real data sets.

Les informations fournies dans la section « A propos du livre » peuvent faire référence à une autre édition de ce titre.

Éditeur
Chapman & Hall/CRC
Date d'édition
1996
Langue
anglais
ISBN 10 
0412056119
ISBN 13 
9780412056116
Reliure
Relié
Nombre de pages
416
Coordonnées du fabricant
non disponible
Personne responsable
non disponible