This paper concerns the use of Markov chain Monte Carlo methods for posterior
sampling in Bayesian nonparametric mixture models with normalized random
measure priors. Making use of some recent posterior characterizations for the
class of normalized random measures, we propose novel Markov chain Monte Carlo
methods of both marginal type and conditional type. The proposed marginal
samplers are generalizations of Neal's well-regarded Algorithm 8 for Dirichlet
process mixture models, whereas the conditional sampler is a variation of those
recently introduced in the literature. For both the marginal and conditional
methods, we consider as a running example a mixture model with an underlying
normalized generalized Gamma process prior, and describe comparative simulation
results demonstrating the efficacies of the proposed methods.

本文提出了一种基于 Markov chain Monte Carlo 方法的贝叶斯非参数混合模型的后验抽样方法，该方法采用归一化随机测度先验，并利用一些最近的后验特征进行修改，提高了 Neal 的 Dirichlet 过程混合模型 8 号算法的效率，并为其构建了一种相关算法。在应用实例方面，我们考虑了具有潜在归一化广义 Gamma 过程先验的混合模型，并描述了比较性的模拟结果证明了所提方法的有效性。