Statistics and Computing

, Volume 20, Issue 4, pp 447-456

First online:

Efficient Markov chain Monte Carlo with incomplete multinomial data

  • Kwang Woo AhnAffiliated withDivision of Biostatistics, Medical College of Wisconsin
  • , Kung-Sik ChanAffiliated withDepartment of Statistics and Actuarial Science, The University of Iowa Email author 

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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.


Blocking Gibbs sampler Dirichlet distribution Epidemiology