Linganisha mbinu

	Utoaji wa Kigezo	Uchambuzi wa Latent Dirichlet (LDA)
Nyanja	Mbinu za Bayes	Ujifunzaji wa Mashine
Familia	Bayesian methods	Latent structure
Mwaka wa asili	1999	2003
Mwanzilishi	Jordan, Ghahramani, Jaakkola & Saul	Blei, D. M.; Ng, A. Y.; Jordan, M. I.
Aina	Approximate Bayesian inference	Generative probabilistic topic model (three-level hierarchical Bayesian)
Chanzo asilia	Jordan, M. I., Ghahramani, Z., Jaakkola, T. S., & Saul, L. K. (1999). An introduction to variational methods for graphical models. Machine Learning, 37(2), 183–233.	Blei, D. M., Ng, A. Y., & Jordan, M. I. (2003). Latent Dirichlet allocation. Journal of Machine Learning Research, 3, 993–1022.
Majina mbadala	VI, variational Bayes, VB, mean-field variational inference	LDA, topic model, Blei-Ng-Jordan model, probabilistic topic modeling
Zinazohusiana	4	3
Muhtasari	Variational inference (VI) is a family of techniques that turn Bayesian posterior computation into an optimisation problem. Instead of drawing samples from the exact posterior — as Markov chain Monte Carlo does — VI posits a simpler, tractable family of distributions and finds the member of that family closest to the true posterior by maximising the evidence lower bound (ELBO). Introduced in its modern graphical-model form by Jordan, Ghahramani, Jaakkola and Saul (1999) and given a comprehensive statistical treatment by Blei, Kucukelbir and McAuliffe (2017), VI is now the standard scalable inference engine in probabilistic machine learning.	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.