Uncertainty in Artificial Intelligence
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Variational Approximations between Mean Field Theory and the Junction Tree Algorithm
Wim Wiegerinck
Abstract:
Recently, variational approximations such as the mean field approximation have received much interest. We extend the standard mean field method by using an approximating distribution that factorises into cluster potentials. This includes undirected graphs, directed acyclic graphs and junction trees. We derive generalized mean field equations to optimize the cluster potentials. We show that the method bridges the gap between the standard mean field approximation and the exact junction tree algorithm. In addition, we address the problem of how to choose the graphical structure of the approximating distribution. From the generalised mean field equations we derive rules to simplify the structure of the approximating distribution in advance without affecting the quality of the approximation. We also show how the method fits into some other variational approximations that are currently popular.
Keywords: Approximate inference, variational methods, mean field theory
Pages: 626-633
PS Link: ftp://www.mbfys.kun.nl/snn/pub/reports/Wiegerinck.UAI2000.ps.Z
PDF Link: /papers/00/p626-wiegerinck.pdf
BibTex:
@INPROCEEDINGS{Wiegerinck00,
AUTHOR = "Wim Wiegerinck ",
TITLE = "Variational Approximations between Mean Field Theory and the Junction Tree Algorithm",
BOOKTITLE = "Proceedings of the Sixteenth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-00)",
PUBLISHER = "Morgan Kaufmann",
ADDRESS = "San Francisco, CA",
YEAR = "2000",
PAGES = "626--633"
}


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