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

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

 
9783846551554: Bayesian Inference for the Simple and Mixture of Laplace Distribution: An application to Industrial Processes
Brossura
ISBN 10: 3846551554 ISBN 13: 9783846551554
Casa editrice: LAP LAMBERT Academic Publishing, 2012

Sinossi

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.

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L'autore

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.

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Editore
LAP LAMBERT Academic Publishing
Data di pubblicazione
2012
Lingua
Inglese
ISBN 10 
3846551554
ISBN 13 
9783846551554
Rilegatura
Copertina flessibile
Numero di pagine
280
Contatto del produttore
non disponibile
Persona responsabile
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