Vertaile menetelmiä

	Bayesiläiset assosiaatiosäännöt	Bayesiläinen Gaussinen sekoitusmalli
Tieteenala	Koneoppiminen	Koneoppiminen
Menetelmäperhe	Machine learning	Machine learning
Syntyvuosi	1994–1995	1999–2006
Kehittäjä	Heckerman, D. et al.; Agrawal, R. & Srikant, R.	Attias, H.; Bishop, C. M.
Tyyppi	Probabilistic rule mining	Probabilistic clustering / density estimation
Alkuperäislähde	Heckerman, D., Geiger, D., & Chickering, D. M. (1995). Learning Bayesian networks: The combination of knowledge and statistical data. Machine Learning, 20(3), 197–243.	Bishop, C. M. (2006). Pattern Recognition and Machine Learning (Ch. 10). Springer. ISBN: 978-0-387-31073-2
Rinnakkaisnimet	Bayesian rule learning, probabilistic association rules, Bayesian itemset mining, BAR	Bayesian GMM, Variational Gaussian Mixture, VBGMM, Dirichlet Process Gaussian Mixture
Liittyvät	6	4
Tiivistelmä	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.