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| Reguły Asocjacyjne Bayesa× | Bayesowski model mieszaniny rozkładów Gaussa× | |
|---|---|---|
| Dziedzina | Uczenie maszynowe | Uczenie maszynowe |
| Rodzina | Machine learning | Machine learning |
| Rok powstania≠ | 1994–1995 | 1999–2006 |
| Twórca≠ | Heckerman, D. et al.; Agrawal, R. & Srikant, R. | Attias, H.; Bishop, C. M. |
| Typ≠ | Probabilistic rule mining | Probabilistic clustering / density estimation |
| Źródło pierwotne≠ | Heckerman, D., Geiger, D., & Chickering, D. M. (1995). Learning Bayesian networks: The combination of knowledge and statistical data. Machine Learning, 20(3), 197–243. DOI ↗ | Bishop, C. M. (2006). Pattern Recognition and Machine Learning (Ch. 10). Springer. ISBN: 978-0-387-31073-2 |
| Inne nazwy | Bayesian rule learning, probabilistic association rules, Bayesian itemset mining, BAR | Bayesian GMM, Variational Gaussian Mixture, VBGMM, Dirichlet Process Gaussian Mixture |
| Pokrewne≠ | 6 | 4 |
| Podsumowanie≠ | Bayesian Association Rules extend classical association rule mining by placing a prior probability distribution over rules and scoring them by their posterior probability given the data. Rather than thresholding on raw support and confidence counts, this Bayesian framework naturally penalises complexity, corrects for multiple comparisons, and produces calibrated probabilistic rule strengths across transactional or categorical datasets. | 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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