方法证据记录
Bayesian Mixture Modeling
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.
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 Finite Mixture Modeling
分类方法记录 · latent-structure / statistics
- Fruhwirth-Schnatter, S., Celeux, G. & Robert, C. P. (Eds.) (2019). Handbook of Mixture Analysis. CRC Press / Chapman & Hall. · ISBN 9780367733995
- Richardson, S. & Green, P. J. (1997). On Bayesian analysis of mixtures with an unknown number of components. Journal of the Royal Statistical Society: Series B, 59(4), 731–792. · DOI 10.1111/1467-9868.00095
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