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Bayesian Gaussian Mixture Model

The Bayesian Gaussian Mixture Model places prior distributions over all mixture parameters and infers their posteriors — typically via Variational Bayes or MCMC — rather than fitting fixed point estimates. This yields principled uncertainty quantification, automatic selection of the effective number of components, and resistance to overfitting small datasets.

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Sources

  1. Bishop, C. M. (2006). Pattern Recognition and Machine Learning (Ch. 10). Springer. ISBN: 978-0-387-31073-2
  2. Attias, H. (1999). Inferring parameters and structure of latent variable models by variational Bayes. Proceedings of the 15th Conference on Uncertainty in Artificial Intelligence (UAI), 21–30. link ↗

Related methods

Gaussian ProcessK-meansSemi-supervised Gaussian Mixture ModelVariational Autoencoder

Referenced by

Active learning Gaussian mixture modelBayesian Association RulesBayesian Autoencoder Anomaly DetectionBayesian Semi-supervised LearningOnline Gaussian Mixture ModelRegularized Gaussian Mixture Model

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ScholarGate — Bayesian Gaussian Mixture Model (Bayesian Gaussian Mixture Model (Variational Bayes / MCMC Inference)). Retrieved 2026-06-04 from https://scholargate.app/tr/machine-learning/bayesian-gaussian-mixture-model