Latent structureMultivariate analysis
贝叶斯潜在类别分析 (Bayesian Latent Class Analysis, BLCA)
贝叶斯潜在类别分析通过为所有模型参数设置先验分布，并使用后验推断（通常通过MCMC）将个体分类到无观察到的类别组中，量化类别成员资格的不确定性，并以原则性、概率性的方式选择类别的数量，从而扩展了经典的LCA。
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来源
- Dunson, D. B. & Xing, C. (2009). Nonparametric Bayes modeling of multivariate categorical data. Journal of the American Statistical Association, 104(487), 1042–1051. DOI: 10.1198/jasa.2009.tm08439 ↗
- White, A. & Murphy, T. B. (2016). BayesLCA: An R package for Bayesian latent class analysis. Journal of Statistical Software, 61(13), 1–28. DOI: 10.18637/jss.v061.i13 ↗
如何引用本页
ScholarGate. (2026, June 3). Bayesian Latent Class Analysis. ScholarGate. https://scholargate.app/zh/statistics/bayesian-latent-class-analysis
Which method?
Set this method beside its closest kin and read them side by side — the library lays the books on the table; the choice is yours.
- 贝叶斯聚类分析统计学↔ compare
- 贝叶斯验证性因子分析 (BCFA)心理测量学↔ compare
- 贝叶斯混合模型统计学↔ compare
- 潜在类别分析 (Latent Class Analysis, LCA)统计学↔ compare
- 潜剖面分析 (Latent Profile Analysis, LPA)心理测量学↔ compare
- 混合模型统计学↔ compare