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A class of Bayes‐optimal two‐stage screens
Author(s) -
Boys R. J.,
Glazebrook K. D.,
Laws D. J.
Publication year - 1996
Publication title -
naval research logistics (nrl)
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.665
H-Index - 68
eISSN - 1520-6750
pISSN - 0894-069X
DOI - 10.1002/(sici)1520-6750(199612)43:8<1109::aid-nav4>3.0.co;2-i
Subject(s) - bayes' theorem , stage (stratigraphy) , computer science , class (philosophy) , set (abstract data type) , sentence , measure (data warehouse) , filter (signal processing) , resource (disambiguation) , sentence completion tests , artificial intelligence , mathematics , bayesian probability , data mining , psychology , biology , social psychology , paleontology , computer network , computer vision , programming language
Items are characterized by a set of attributes ( T ) and a collection of covariates ( X ) associated with those attributes. We wish to screen for acceptable items ( T ∈ C T ), but T is expensive to measure. We envisage a two‐stage screen in which observation of X _ is used as a filter at the first stage to sentence most items. The second stage involves the observation of T for those items for which the first stage is indecisive. We adopt a Bayes decision‐theoretic approach to the development of optimal two‐stage screens within a general framework for costs and stochastic structure. We also consider the important question of how much screens need to be modified in the light of resource limitations that bound the proportion of items that can be passed to the second stage. © 1996 John Wiley & Sons, Inc.