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| Latent Dirichlet Allocation (LDA)× | Chaîne de Markov Monte Carlo (MCMC)× | |
|---|---|---|
| Domaine≠ | Apprentissage automatique | Bayésien |
| Famille≠ | Latent structure | Bayesian methods |
| Année d'origine≠ | 2003 | — |
| Auteur d'origine≠ | Blei, D. M.; Ng, A. Y.; Jordan, M. I. | — |
| Type≠ | Generative probabilistic topic model (three-level hierarchical Bayesian) | Posterior sampling algorithm |
| Source fondatrice≠ | Blei, D. M., Ng, A. Y., & Jordan, M. I. (2003). Latent Dirichlet allocation. Journal of Machine Learning Research, 3, 993–1022. DOI ↗ | 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 |
| Alias≠ | LDA, topic model, Blei-Ng-Jordan model, probabilistic topic modeling | markov chain monte carlo, MCMC sampling, MCMC (Markov Zinciri Monte Carlo) |
| Apparentées | 3 | 3 |
| Résumé≠ | 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. | Markov Chain Monte Carlo (MCMC) is a family of computational algorithms for sampling from complex probability distributions, most commonly the posterior distributions that arise in Bayesian inference. Rather than computing posteriors analytically — which is rarely possible for realistic models — MCMC constructs a Markov chain whose stationary distribution is the target posterior and draws dependent samples from it, enabling full probabilistic inference for virtually any model. |
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