Uncertainty in Artificial Intelligence
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A Variational Approximation for Bayesian Networks with Discrete and Continuous Latent Variables
Kevin Murphy
Abstract:
We show how to use a variational approximation to the logistic function to perform approximate inference in Bayesian networks containing discrete nodes with continuous parents. Essentially, we convert the logistic function to a Gaussian, which facilitates exact inference, and then iteratively adjust the variational parameters to improve the quality of the approximation. We demonstrate experimentally that this approximation is faster and potentially more accurate than sampling. We also introduce a simple new technique for handling evidence, which allows us to handle arbitrary distributions on observed nodes, as well as achieving a significant speedup in networks with discrete variables of large cardinality.
Keywords: hybrid networks, discrete nodes with continuous parents, variational approximation, h
Pages: 457-466
PS Link: http://www.cs.berkeley.edu/~murphyk/Papers/uai99_hybrid.ps
PDF Link: /papers/99/p457-murphy.pdf
BibTex:
@INPROCEEDINGS{Murphy99,
AUTHOR = "Kevin Murphy ",
TITLE = "A Variational Approximation for Bayesian Networks with Discrete and Continuous Latent Variables",
BOOKTITLE = "Proceedings of the Fifteenth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-99)",
PUBLISHER = "Morgan Kaufmann",
ADDRESS = "San Francisco, CA",
YEAR = "1999",
PAGES = "457--466"
}


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