In this paper, the forward selected first‐order Markov mixture (FSFOMM) is proposed for modelling heterogeneous categorical sequences with secondary components capable of detecting outlying sequences within each cluster. Such sequences are assumed to have different transition probabilities in certain states. The model provides an attractive and flexible tool for diagnostics of unusual behaviours and parsimonious modelling of transition probabilities. The algorithm is tested on simulated as well as real‐life datasets with promising results.

中文翻译：

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具有次要成分的分类序列的混合建模

在本文中，提出了前向选择的一阶马尔可夫混合物（FSFOMM），用于建模具有可检测每个聚类中离群序列的次级成分的异类分类序列。假设此类序列在某些状态下具有不同的转移概率。该模型为异常行为的诊断和过渡概率的简约建模提供了一种有吸引力且灵活的工具。该算法已在模拟数据集和实际数据集上进行了测试，结果令人满意。