Statistics and Computing

, Volume 20, Issue 4, pp 447–456

Efficient Markov chain Monte Carlo with incomplete multinomial data


DOI: 10.1007/s11222-009-9136-9

Cite this article as:
Ahn, K.W. & Chan, KS. Stat Comput (2010) 20: 447. doi:10.1007/s11222-009-9136-9


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.


BlockingGibbs samplerDirichlet distributionEpidemiology

Copyright information

© Springer Science+Business Media, LLC 2009

Authors and Affiliations

  1. 1.Division of BiostatisticsMedical College of WisconsinMilwaukeeUSA
  2. 2.Department of Statistics and Actuarial ScienceThe University of IowaIowa CityUSA