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	ベイズ混合モデリング	混合モデル (Mixture Modeling)
分野	統計学	統計学
系統	Latent structure	Latent structure
提唱年	1997 (Richardson & Green Bayesian formulation)	1894
提唱者	Richardson & Green (seminal Bayesian treatment, 1997); broader Bayesian mixture roots trace to Dempster, Laird & Rubin (EM, 1977) and Titterington, Smith & Makov (1985)	Karl Pearson
種類	Latent-class / model-based clustering	Latent variable / density estimation
原典	Fruhwirth-Schnatter, S., Celeux, G. & Robert, C. P. (Eds.) (2019). Handbook of Mixture Analysis. CRC Press / Chapman & Hall. ISBN: 9780367733995	McLachlan, G. J. & Peel, D. (2000). Finite Mixture Models. Wiley-Interscience. ISBN: 978-0471006268
別名	Bayesian mixture model, BMM, Bayesian model-based clustering, Bayesian finite mixture	finite mixture model, mixture distribution model, FMM, model-based clustering
関連	4	6
概要	Bayesian mixture modeling represents the population as a weighted sum of K component distributions and estimates all unknowns — mixing weights, component parameters, and even the number of components — through posterior inference. It extends classical mixture analysis by placing priors on every parameter and quantifying uncertainty over latent group assignments rather than treating them as fixed.	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.