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Efficient Markov chain Monte Carlo with incomplete multinomial data

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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.

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Authors and Affiliations

  1. Division of Biostatistics, Medical College of Wisconsin, Milwaukee, WI, 53226, USA

    Kwang Woo Ahn

  2. Department of Statistics and Actuarial Science, The University of Iowa, Iowa City, IA, 52242, USA

    Kung-Sik Chan

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  1. Kwang Woo Ahn
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  2. Kung-Sik Chan
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Correspondence to Kung-Sik Chan.

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Ahn, K.W., Chan, KS. Efficient Markov chain Monte Carlo with incomplete multinomial data. Stat Comput 20, 447–456 (2010). https://doi.org/10.1007/s11222-009-9136-9

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  • DOI: https://doi.org/10.1007/s11222-009-9136-9

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