班级(哲学)
潜在类模型
概率逻辑
集合(抽象数据类型)
计算机科学
人口
数据科学
数据挖掘
机器学习
风险分析(工程)
人工智能
医学
环境卫生
程序设计语言
作者
Kayvan Aflaki,Simone N. Vigod,Joel G. Ray
标识
DOI:10.1016/j.jclinepi.2022.05.008
摘要
Latent class analysis (LCA) offers a powerful analytical approach for categorizing groups (or "classes") within a heterogenous population. LCA identifies these hidden classes by a set of predefined features, known as "indicators". Unlike many other grouping analytical approaches, LCA derives classes using a probabilistic approach. In this first paper, we describe the common applications of LCA, and outline its advantages over other analytical subgrouping methods.
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