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| Expectation Propagation (EP)× | Latent Dirichlet Allocation (LDA)× | |
|---|---|---|
| Campo≠ | Bayesiano | Apprendimento automatico |
| Famiglia≠ | Bayesian methods | Latent structure |
| Anno di origine≠ | 2001 | 2003 |
| Ideatore≠ | Thomas P. Minka | Blei, D. M.; Ng, A. Y.; Jordan, M. I. |
| Tipo≠ | Approximate inference algorithm | Generative probabilistic topic model (three-level hierarchical Bayesian) |
| Fonte seminale≠ | 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. link ↗ | Blei, D. M., Ng, A. Y., & Jordan, M. I. (2003). Latent Dirichlet allocation. Journal of Machine Learning Research, 3, 993–1022. DOI ↗ |
| Alias≠ | EP, expectation propagation, EP algorithm, assumed-density filtering generalisation | LDA, topic model, Blei-Ng-Jordan model, probabilistic topic modeling |
| Correlati | 3 | 3 |
| Sintesi≠ | 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. |
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