Bayesian methods
Dirichlet Process Mixture Model
Dirichlet Process Mixture Model (DPMM) 是一种非参数贝叶斯聚类方法，通过 Ferguson (1973) 引入的 Dirichlet 过程先验来放置一个关于分布的概率分布。与有限混合模型不同，DPMM 不需要分析者预先指定簇的数量；相反，它从数据中推断分量的数量，允许一个随着更多观测值的到来而增长的有效无界混合。
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来源
- Ferguson, T. S. (1973). A Bayesian analysis of some nonparametric problems. The Annals of Statistics, 1(2), 209–230. DOI: 10.1214/aos/1176342360 ↗
- Neal, R. M. (2000). Markov chain sampling methods for Dirichlet process mixture models. Journal of Computational and Graphical Statistics, 9(2), 249–265. DOI: 10.1080/10618600.2000.10474879 ↗
- Hjort, N. L., Holmes, C., Müller, P., & Walker, S. G. (Eds.) (2010). Bayesian Nonparametrics. Cambridge University Press. ISBN: 978-0-521-51346-3
如何引用本页
ScholarGate. (2026, June 3). Dirichlet Process Mixture Model. ScholarGate. https://scholargate.app/zh/bayesian/dirichlet-process-mixture-model
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