Parallel Computing with a Bayesian Item Response Model

• 作者: Kyriakos Patsias Mona Rahimi Shahram Rahimi Yanyan Sheng
• 来源期刊: 美国计算数学期刊(英文)
• 学科: 工学
• 关键词: Gibbs Sampling High Performance Computing Message PASSING Interface TWO-PARAMETER IRT Model
• 年，卷（期）: 2012,（2）
• 页码范围: 65-71
• 页数: 7页
• 分类号: TP39

摘要

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.