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An adaptive hierarchical Bayes quality measurement plan
Author(s) -
Lahiri Partha,
Li Huilin
Publication year - 2009
Publication title -
applied stochastic models in business and industry
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.413
H-Index - 40
eISSN - 1526-4025
pISSN - 1524-1904
DOI - 10.1002/asmb.778
Subject(s) - bayes' theorem , quality assurance , computer science , quality (philosophy) , hyperparameter , sampling (signal processing) , parametric statistics , plan (archaeology) , audit , reliability engineering , operations research , data mining , statistics , bayesian probability , machine learning , operations management , artificial intelligence , mathematics , economics , engineering , filter (signal processing) , accounting , philosophy , external quality assessment , archaeology , epistemology , computer vision , history
The quality of a production process is often judged by a quality assurance audit, which is essentially a structured system of sampling inspection plan. The defects of sampled products are assessed and compared with a quality standard, which is determined from a tradeoff among manufacturing costs, operating costs and customer needs. In this paper, we propose a new hierarchical Bayes quality measurement plan that assumes an implicit prior for the hyperparameters. The resulting posterior means and variances are obtained adaptively using a parametric bootstrap method. Published in 2009 by John Wiley & Sons, Ltd.
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