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In this paper we demonstrate that tempering Markov chain Monte Carlo samplers for Bayesian models by recursively subsampling observations without replacement can improve the performance of baseline samplers in terms of effective sample size per computation.
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A. W. Metropolis, A. W. Rosenbluth, M. N. Rosenbluth, A. H. Teller, and E. Teller · 1953
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In Steve Brooks, Andrew Gelman, Galin Jones, and Xiao-Li Meng, editors, Handbook of Markov Chain Monte Carlo , chapter 11, pages 295–311. Chapman and Hall/CRC, May
Charles J Geyer
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