Effective component relabeling in Bayesian analyses of mixture models is critical to the routine use of mixtures in classification with analysis based on Markov chain Monte Carlo methods. The classification-based relabeling approach here is computationally attractive and statistically effective, and scales well with sample size and number of mixture components concordant with enabling routine analyses of increasingly large datasets. Building on the best of existing methods, practical relabeling aims to match data:component classification indicators in MCMC iterates with those of a defined reference mixture distribution. The method performs as well as or better than existing methods in small dimensional problems, while being practically superior in problems with larger datasets as the approach is scalable. We describe examples and computational benchmarks, and provide supporting code with efficient computational implementation of the algorithm that will be of use to others in practical applications of mixture models.

中文翻译：

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混合模型中基于分类的高效重新标记

混合模型贝叶斯分析中的有效成分重新标记对于在基于马尔可夫链蒙特卡罗方法的分析中常规使用混合分类至关重要。这里基于分类的重新标记方法在计算上具有吸引力且统计上有效，并且可以很好地扩展样本大小和混合成分的数量，从而能够对越来越大的数据集进行常规分析。在现有方法的最佳基础上，实用的重新标记旨在匹配数据：MCMC 中的组件分类指标与定义的参考混合物分布的指标进行迭代。该方法在小维度问题上的表现与现有方法一样好或更好，同时由于该方法具有可扩展性，因此在处理较大数据集的问题上实际上更胜一筹。