Efficient Markov chain Monte Carlo with incomplete multinomial data

Authors:
Kwang Woo Ahn;Kung-Sik Chan
Affiliations:
Division of Biostatistics, Medical College of Wisconsin, Milwaukee, USA 53226;Department of Statistics and Actuarial Science, The University of Iowa, Iowa City, USA 52242
Venue:
Statistics and Computing
Year:
2010

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Abstract

We propose a block Gibbs sampling scheme for incomplete multinomial data. We show that the new approach facilitates maximal blocking, thereby reducing serial dependency and speeding up the convergence of the Gibbs sampler. We compare the efficiency of the new method with the standard, non-block Gibbs sampler via a number of numerical examples.