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Bayesian Inference for Multinomial Group Testing
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 Title & Authors
Bayesian Inference for Multinomial Group Testing
Heo, Tae-Young; Kim, Jong-Min;
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 Abstract
This paper consider trinomial group testing concerned with classification of N given units into one of k disjoint categories. In this paper, we propose Bayesian inference for estimating individual category proportions using the trinomial group testing model proposed by Bar-Lev et al. (2005). We compared a relative efficience (RE) based on the mean squared error (MSE) of MLE and Bayes estimators with various prior information. The impact of different prior specifications on the estimates is also investigated using selected prior distribution. The impact of different priors on the Bayes estimates is modest when the sample size and group size we large.
 Keywords
Group testing;trinomial distribution;interval estimator;Bayesian;Dirichlet distribution;
 Language
English
 Cited by
 References
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