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.