Alternating Subspace-Spanning Resampling to Accelerate Markov Chain Monte Carlo Simulation
- 1 March 2003
- journal article
- Published by Taylor & Francis in Journal of the American Statistical Association
- Vol. 98 (461) , 110-117
- https://doi.org/10.1198/016214503388619148
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.Keywords
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