A High Performance Gibbs Sampling Algorithm for Item Response Theory
نویسندگان
چکیده
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.
منابع مشابه
High performance parallel Gibbs sampling for IRT models
• Item Response Theory (IRT) yields models that describe a probabilistic relationship between correct responses on a set of items and a latent trait. • We can apply Gibbs sampling to the two-parameter normal model, however a large number of iterations is needed for the Markov chain to converge. • So, the algorithm is computationally intensive and demands significant execution time. • This fact ...
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