Módszerek összehasonlítása

	Expectation Propagation (EP)	Latens Dirichlet-eloszlás (LDA)
Tudományterület	Bayes-statisztika	Gépi tanulás
Módszercsalád	Bayesian methods	Latent structure
Keletkezés éve	2001	2003
Megalkotó	Thomas P. Minka	Blei, D. M.; Ng, A. Y.; Jordan, M. I.
Típus	Approximate inference algorithm	Generative probabilistic topic model (three-level hierarchical Bayesian)
Alapmű	Minka, T. P. (2001). Expectation propagation for approximate Bayesian inference. In Proceedings of the Seventeenth Conference on Uncertainty in Artificial Intelligence (UAI-01), pp. 362–369. Morgan Kaufmann.	Blei, D. M., Ng, A. Y., & Jordan, M. I. (2003). Latent Dirichlet allocation. Journal of Machine Learning Research, 3, 993–1022.
Alternatív nevek	EP, expectation propagation, EP algorithm, assumed-density filtering generalisation	LDA, topic model, Blei-Ng-Jordan model, probabilistic topic modeling
Kapcsolódó	3	3
Összefoglaló	Expectation Propagation (EP) is a deterministic message-passing algorithm for approximate posterior inference in Bayesian models, introduced by Thomas P. Minka at UAI 2001. It iteratively refines a set of local approximate factors — each drawn from the exponential family — so that their product closely matches the true intractable posterior, achieving higher accuracy than mean-field variational inference on many probabilistic machine learning tasks.	Latent Dirichlet Allocation (LDA) is a generative probabilistic model for collections of discrete data, introduced by Blei, Ng, and Jordan in 2003. It treats each document as a mixture of latent topics and each topic as a probability distribution over words, enabling unsupervised discovery of thematic structure across large text corpora. It is one of the most cited papers in machine learning and natural language processing.