M. I. Jordan, Zoubin Ghahramani, Tommi S. Jaakkola, Lawrence K. Saul  Home/Search
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Abstract: This paper presents a tutorial introduction to the use of variational methods for inference and learning in graphical models. We present a number of examples of graphical models, including the QMR-DT database, the sigmoid belief network, the Boltzmann machine, and several variants of hidden Markov models, in which it is infeasible to run exact inference algorithms. We then introduce variational methods, showing how upper and lower bounds can be found for local probabilities, and discussing... (Update)
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BibTeX entry: (Update)
Jordan, M., Ghaharamani, Z. Jaakkola, T., and Saul, L. (1998). An introduction to variational methods for graphical models. In M. I. Jordan (Ed.), Learning in Graphical Models. http://citeseer.ist.psu.edu/article/jordan98introduction.html More
@article{ jordan99introduction,
author = "Michael I. Jordan and Zoubin Ghahramani and Tommi Jaakkola and Lawrence K. Saul",
title = "An Introduction to Variational Methods for Graphical Models",
journal = "Machine Learning",
volume = "37",
number = "2",
pages = "183-233",
year = "1999",
url = "citeseer.ist.psu.edu/article/jordan98introduction.html" }
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