K-adaptive clustering methods of general discrete random structure through generalized Bayes framework

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:2023-11-29 10:00





The prior likelihood of generalized Bayes framework for probabilistic clustering through general discrete random structure is developed, so that the framework can contain the background of both K-fixed and K-adaptive clustering methods, such as K-means and Gibbs partition. The relative derivations including the classifier, posterior inference and detailed framework adjustments are deduced. A modified algorithm combining the ideas of K-dissimilarity algorithm and reversible jump Markov chain Monte Carlo method is proposed to search for global optimization. By iterations on re-clustering through Gibbs sampling method, the stationary distribution of clustering likelihood is obtained for uncertainty quantification, where the choice of K can be controlled by QAIC and parametric setting in different K adaptive methods. Finally, it can be shown that the clustering results in application excellently divide the samples into groups with the satisfying number of categories and high accuracy.


左国新,统计学博士,华中师范大学教授,数学与统计学学院副院长。研究范围涵盖了生存分析、离散数据特别是秩数据、统计计算等。目前主要从事非参数/半参数回归模型的统计推断及复杂数据的统计分析。兼任湖北省现场统计研究会副理事长,中国现场统计学会资源与环境分会常务理事,中国商业统计学会常务理事。2001-2002年在香港中文大学进行合作研究。 2007年在香港中文大学获得统计学博士学位;2010-2011年在美国Rochester大学从事博士后研究。在Lifetime data analysis, Communication in Statistics, Theory and methods, Journal of Nonparametric Statistics, Journal of Statistical Planning and Inference, Suicide and Life-Threatening Behavior等杂志发表论文20余篇,主持和参与国家自然科学基金面上项目多项。