An Introduction to Variational Methods for Graphical Models

by: Michael I Jordan, Zoubin Ghahramani, Tommi Jaakkola, Lawrence K Saul

Machine Learning, Vol. 37, No. 2. (1999), pp. 183-233.

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Abstract

. This paper presents a tutorial introduction to the use of variational methods for inference and learning in graphical models (Bayesian networks and Markov random fields). 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, which exploit laws of large numbers to...

@article{citeulike:420107, abstract = {. This paper presents a tutorial introduction to the use of variational methods for inference and learning in graphical models (Bayesian networks and Markov random fields). 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, which exploit laws of large numbers to...}, author = {Jordan, Michael I. and Ghahramani, Zoubin and Jaakkola, Tommi and Saul, Lawrence K. }, citeulike-article-id = {420107}, journal = {Machine Learning}, keywords = {graphical\_models, statistical\_learning, statistics, variational\_methods}, number = {2}, pages = {183--233}, posted-at = {2008-03-24 12:40:39}, priority = {2}, title = {An Introduction to Variational Methods for Graphical Models}, url = {http://citeseer.ist.psu.edu/jordan98introduction.html}, volume = {37}, year = {1999} }

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TY - JOUR ID - citeulike:420107 L3 - citeulike-article-id:420107 TI - An Introduction to Variational Methods for Graphical Models JF - Machine Learning VL - 37 IS - 2 SP - 183 EP - 233 N2 - . This paper presents a tutorial introduction to the use of variational methods for inference and learning in graphical models (Bayesian networks and Markov random fields). 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, which exploit laws of large numbers to... KW - graphical_models KW - statistical_learning KW - statistics KW - variational_methods AU - Jordan, Michael AU - Ghahramani, Zoubin AU - Jaakkola, Tommi AU - Saul, Lawrence PY - 1999/// UR - http://citeseer.ist.psu.edu/jordan98introduction.html ER -

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