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We evaluate MCMC sampling schemes for a variety of link functions in generalized linear models with Dirichlet process random effects. First, we find that there is a large amount of variability in the performance of MCMC algorithms, with the slice sampler typically being less desirable than either a Kolmogorov-Smirnov mixture representation or a Metropolis-Hastings algorithm. Second, in fitting the Dirichlet process, dealing with the precision parameter has troubled model specifications in thedoi:10.1007/s10260-011-0168-x fatcat:3pqxclu7zrgvpbnt6lvoxgzmqy