A note on posterior sampling from Dirichlet mixture models
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In this note we observe that the recent MCMC methods of Papaspiliopoulos &
Roberts (2008) and Walker (2007) for Dirichlet mixture models are intrinsically con-
nected and can be naturally combined to yield an algorithm which is better (in terms of
mixing), faster (in terms of execution time) and easier (in terms of implementation and
coding) than either of them.
[1] L. Devroye. Non-Uniform Random Variate Generation , 1986 .
[2] Stephen G. Walker,et al. Sampling the Dirichlet Mixture Model with Slices , 2006, Commun. Stat. Simul. Comput..