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A Bayesian Approach to a Logistic Regression Model with Incomplete Information

โœ Scribed by Taeryon Choi; Mark J. Schervish; Ketra A. Schmitt; Mitchell J. Small


Book ID
109223805
Publisher
John Wiley and Sons
Year
2008
Tongue
English
Weight
271 KB
Volume
64
Category
Article
ISSN
0006-341X

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Incomplete knowledge refers to situations in which a decision maker can rank the probabilities of occurrence for events of interest, but cannot specify these probabilities exactly. In this paper a Monte Carlo approach is used to investigate how physicians can arrive at revised or posterior rankings