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High Performance Gibbs Sampling for IRT Models Using Row-Wise Decomposition
Joint Authors
Source
ISRN Computational Mathematics
Issue
Vol. 2012, Issue 2012 (31 Dec. 2012), pp.1-9, 9 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2012-12-12
Country of Publication
Egypt
No. of Pages
9
Main Subjects
Abstract EN
Item response theory (IRT) is a popular approach used for addressing statistical problems in psychometrics as well as in other fields.
The fully Bayesian approach for estimating IRT models is computationally expensive.
This limits the use of the procedure in real applications.
In an effort to reduce the execution time, a previous study shows that high performance computing provides a solution by achieving a considerable speedup via the use of multiple processors.
Given the high data dependencies in a single Markov chain for IRT models, it is not possible to avoid communication overhead among processors.
This study is to reduce communication overhead via the use of a row-wise decomposition scheme.
The results suggest that the proposed approach increased the speedup and the efficiency for each implementation while minimizing the cost and the total overhead.
This further sheds light on developing high performance Gibbs samplers for more complicated IRT models.
American Psychological Association (APA)
Sheng, Yanyan& Rahimi, Mona. 2012. High Performance Gibbs Sampling for IRT Models Using Row-Wise Decomposition. ISRN Computational Mathematics،Vol. 2012, no. 2012, pp.1-9.
https://search.emarefa.net/detail/BIM-458615
Modern Language Association (MLA)
Sheng, Yanyan& Rahimi, Mona. High Performance Gibbs Sampling for IRT Models Using Row-Wise Decomposition. ISRN Computational Mathematics No. 2012 (2012), pp.1-9.
https://search.emarefa.net/detail/BIM-458615
American Medical Association (AMA)
Sheng, Yanyan& Rahimi, Mona. High Performance Gibbs Sampling for IRT Models Using Row-Wise Decomposition. ISRN Computational Mathematics. 2012. Vol. 2012, no. 2012, pp.1-9.
https://search.emarefa.net/detail/BIM-458615
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-458615