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- W1487271418 abstract "Item response theory (IRT) is a modern test theory that has been used in various aspects of educational and psychological measurement. The fully Bayesian approach shows promise for estimating IRT models. Given that it is computationally expensive, the procedure is limited in real applications. It is hence important to seek ways to reduce the execution time, and therefore, a suitable solution is the use of high performance computing. This study modifies the existing fully Bayesian algorithm for an IRT model so that it can be implemented on a high performance parallel machine. Empirical results suggest that this parallel version of the algorithm achieves a considerable speedup and thus reduces the execution time considerably." @default.
- W1487271418 created "2016-06-24" @default.
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- W1487271418 date "2009-01-01" @default.
- W1487271418 modified "2023-09-26" @default.
- W1487271418 title "A high performance Gibbs-sampling algorithm for item response theory models" @default.
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