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Alternating Subspace-Spanning Resampling to Accelerate Markov Chain Monte Carlo Simulation

Chuanhai Liu
Journal of the American Statistical Association
Vol. 98, No. 461 (Mar., 2003), pp. 110-117
Stable URL: http://www.jstor.org/stable/30045199
Page Count: 8
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Alternating Subspace-Spanning Resampling to Accelerate Markov Chain Monte Carlo Simulation
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Abstract

This article provides a simple method to accelerate Markov chain Monte Carlo sampling algorithms, such as the data augmentation algorithm and the Gibbs sampler, via alternating subspace-spanning resampling (ASSR). The ASSR algorithm often shares the simplicity of its parent sampler but has dramatically improved efficiency. The methodology is illustrated with Bayesian estimation for analysis of censored data from fractionated experiments. The relationships between ASSR and existing methods are also discussed.

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