Gibbs sampling block updating bentonville dating
Specifically, of concern are the statistical complexities that can often arise when item and person parameters are simultaneously estimated (see [1, 17–19]).More recent attention has focused on the fully Bayesian estimation where Markov chain Monte Carlo (MCMC, [20, 21]) simulation techniques are used.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.
Item response theory (IRT) is a popular approach used for addressing statistical problems in psychometrics as well as in other fields.
In their study, the implementation of parallel computing was realized through decomposition of data matrices and item parameters into columns while minimizing the communication overhead among processors.
In their implementation, the person parameters were communicated between the root and the processor nodes.
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.
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