An Introduction to Variational Methods for Graphical Models

  title={An Introduction to Variational Methods for Graphical Models},
  author={Michael I. Jordan and Zoubin Ghahramani and Tommi S. Jaakkola and Lawrence K. Saul},
  journal={Machine Learning},
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… CONTINUE READING
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