Bayesian Inference for the Simple and Mixture of Laplace Distribution: An application to Industrial Processes

Ali, Sajid; Aslam, Muhammad; Kazmi, Syed Mohsin Ali

ISBN 10: 3846551554 / ISBN 13: 9783846551554

Published by LAP LAMBERT Academic Publishing, 2012

About this Item

PRINT ON DEMAND.

Seller Inventory # 18394694190

Bibliographic Details

• Title: Bayesian Inference for the Simple and Mixture of Laplace Distribution: An application to Industrial Processes
• Publisher: LAP LAMBERT Academic Publishing
• Publication Date: 2012
• Language: English
• ISBN 10: 3846551554
• ISBN 13: 9783846551554
• Binding: Soft cover
• Condition: New

About this title

Synopsis

The scientific scuffle over Bayesian is less well known but lasted far longer, for 150 years. It concerned a broader and more essential issue: how we analyze facts, change our minds as we get new information, and make coherent decisions in the face of uncertainty. Bayes rule appears to be a frank, just one line theorem: by updating our preliminary viewpoint with objective new information, we get a new and enhanced confidence. To its adherents, it is an elegant statement about learning from experience. In this book we considered the homogenous and heterogeneous population for the Laplace lifetime model. The Bayesian approach is used and model is analyzed using various informative and noninformative priors under different loss functions. This book can play a role of teacher for the practitioner(s) who wants to learn the elegant Bayesian methodology. Extensive references to journal articles and other technical literature should assist the reader in applying the methods described in this book. We hope that readers will enjoy this book.

About the Author

Sajid Ali is a PhD candidate at Department of Decision Sciences, Bocconi University Milan, Italy. He received his MPhil Statistics degree from Quaid-I-Azam University, Islamabad, Pakistan, in 2010. His research interest includes Bayesian statistics, mixture models, reliability theory, statistical quality control, and process capability analysis.