Comparer des méthodes

	Régression bayésienne	Propagation des attentes (EP)	Latent Dirichlet Allocation (LDA)
Domaine	Bayésien	Bayésien	Apprentissage automatique
Famille	Bayesian methods	Bayesian methods	Latent structure
Année d'origine	—	2001	2003
Auteur d'origine	—	Thomas P. Minka	Blei, D. M.; Ng, A. Y.; Jordan, M. I.
Type	Bayesian linear model	Approximate inference algorithm	Generative probabilistic topic model (three-level hierarchical Bayesian)
Source fondatrice	Gelman, A., Carlin, J. B., Stern, H. S., Dunson, D. B., Vehtari, A. & Rubin, D. B. (2013). Bayesian Data Analysis (3rd ed.). CRC Press. ISBN: 978-1439840955	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.
Alias	bayesian linear regression, probabilistic regression, bayesian regresyon	EP, expectation propagation, EP algorithm, assumed-density filtering generalisation	LDA, topic model, Blei-Ng-Jordan model, probabilistic topic modeling
Apparentées	2	3	3
Résumé	Bayesian regression is a probabilistic version of linear regression that treats the model parameters as uncertain quantities. Instead of returning a single best-fit estimate, it combines prior knowledge with the observed data to produce a full posterior probability distribution for each parameter, from which credible intervals and predictions are read off.	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.