方法证据记录
Bayesian Cluster Analysis
Bayesian cluster analysis assigns observations to latent groups by combining a probabilistic model of within-cluster data with prior beliefs about cluster parameters and the number of clusters. It yields posterior probabilities of cluster membership and principled uncertainty estimates, making it more transparent than classical distance-based clustering algorithms.
源记录
引文逐字复制自方法源记录。这些引文不代表任何层级的验证。
Bayesian Cluster Analysis
分类方法记录 · latent-structure / statistics
- Fraley, C. & Raftery, A. E. (2002). Model-based clustering, discriminant analysis, and density estimation. Journal of the American Statistical Association, 97(458), 611–631. · DOI 10.1198/016214502760047131
- Lau, J. W. & Green, P. J. (2007). Bayesian model-based clustering procedures. Journal of Computational and Graphical Statistics, 16(3), 526–558. · DOI 10.1198/106186007X238855
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