Bajezijanski model Gausovih smeša
Bajezijanski model Gausovih smeša (Bayesian Gaussian Mixture Model) postavlja apriorne raspodele na sve parametre smeše i izračunava njihove aposteriorne raspodele — tipično putem Varijacionog Bejza (Variational Bayes) ili MCMC — umesto prilagođavanja fiksnih procena tačaka. Ovo daje principijelno kvantifikovanje neizvesnosti, automatski izbor efektivnog broja komponenti i otpornost na preprilagođavanje malim skupovima podataka.
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Izvori
- Bishop, C. M. (2006). Pattern Recognition and Machine Learning (Ch. 10). Springer. ISBN: 978-0-387-31073-2
- 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 ↗
Kako citirati ovu stranicu
ScholarGate. (2026, June 3). Bayesian Gaussian Mixture Model (Variational Bayes / MCMC Inference). ScholarGate. https://scholargate.app/sr/machine-learning/bayesian-gaussian-mixture-model
Which method?
Set this method beside its closest kin and read them side by side — the library lays the books on the table; the choice is yours.
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- K-means algoritam klasterovanjaMašinsko učenje↔ compare
- Polu-nadgledovani model Gausovih mešavinaMašinsko učenje↔ compare
- Varijacioni autoenkoderDuboko učenje↔ compare
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