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Birnbaum, “Statistical Theory for Logistic Mental Test Models with a Prior Distribution of Ability,” Journal of Mathematical Psychology, Vol. 6, No. 2, 1969, pp. 258- 276. doi:10.1016/0022-2496(69)90005-4

has been cited by the following article:

• TITLE: Parallel Computing with a Bayesian Item Response Model

  AUTHORS: Kyriakos Patsias, Mona Rahimi, Yanyan Sheng, Shahram Rahimi

  KEYWORDS: Gibbs Sampling; High Performance Computing; Message Passing Interface; Two-Parameter IRT Model

  JOURNAL NAME: American Journal of Computational Mathematics, Vol.2 No.2, June 21, 2012

  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 computation- ally expensive, the procedure is limited in practical applications. It is hence important to seek ways to reduce the execution time. A suitable solution is the use of high performance computing. This study focuses on the fully Bayesian algorithm for a conventional 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.