Latent structureMultivariate analysis
Mixture Modeling
Mixture modeling assumes that a population is composed of K unobserved subpopulations, each described by its own probability distribution. The observed data are treated as draws from a weighted combination of these component distributions. It provides a principled, model-based alternative to ad hoc clustering and supports formal comparison of solutions with different numbers of components.
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Sources
- McLachlan, G. J. & Peel, D. (2000). Finite Mixture Models. Wiley-Interscience. ISBN: 978-0471006268
- 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 ↗
Related methods
Referenced by
Bayesian Cluster AnalysisBayesian Conjoint AnalysisBayesian Hierarchical ClusteringBayesian K-means clusteringBayesian Latent Class AnalysisBayesian Mixture ModelingCluster AnalysisLatent Class AnalysisRobust Conjoint AnalysisRobust Hierarchical ClusteringRobust K-means ClusteringRobust Latent Class AnalysisRobust Latent Profile AnalysisRobust Mixture Modeling