Bayesian Analysis of the Heterogeneity Model
研究异质性模型（有限混合随机效应模型）的贝叶斯估计，比较不同MCMC采样器的性质，处理标签可识别性问题，并通过度量联合分析案例进行说明。
We consider Bayesian estimation of a finite mixture of models with random effects, which is also known as the heterogeneity model. First, we discuss the properties of various Markov chain Monte Carlo samplers that are obtained from full conditional Gibbs sampling by grouping and collapsing. Whereas full conditional Gibbs sampling turns out to be sensitive to the parameterization chosen for the mean structure of the model, the alternative sampler is robust in this respect. However, the logical extension of the approach to the sampling of the group variances does not further increase the efficiency of the sampler. Second, we deal with the identifiability problem due to the arbitrary labeling within the model. Finally, a case study involving metric conjoint analysis serves as a practical illustration.