Uncertainty in Artificial Intelligence
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Exact Inference in Networks with Discrete Children of Continuous Parents
Uri Lerner, Eran Segal, Daphne Koller
Abstract:
Many real life domains contain a mixture of discrete and continuous variables and can be modeled as hybrid Bayesian Networks. Animportant subclass of hybrid BNs are conditional linear Gaussian (CLG) networks, where the conditional distribution of the continuous variables given an assignment to the discrete variables is a multivariate Gaussian. Lauritzen's extension to the clique tree algorithm can be used for exact inference in CLG networks. However, many domains also include discrete variables that depend on continuous ones, and CLG networks do not allow such dependencies to berepresented. No exact inference algorithm has been proposed for these enhanced CLG networks. In this paper, we generalize Lauritzen's algorithm, providing the first ``exact'' inference algorithm for augmented CLG networks --- networks where continuous nodes are conditional linear Gaussians but that also allow discrete children ofcontinuous parents. Our algorithm is exact in the sense that it computes the exact distributions over the discrete nodes, and the exact first and second moments of the continuous ones, up to the accuracy obtained by numerical integration used within thealgorithm. When the discrete children are modeled with softmax CPDs (as is the case in many real world domains) the approximation of the continuous distributions using the first two moments is particularly accurate. Our algorithm is simple to implement and often comparable in its complexity to Lauritzen's algorithm. We show empirically that it achieves substantially higher accuracy than previous approximate algorithms.
Keywords:
Pages: 319-328
PS Link:
PDF Link: /papers/01/p319-lerner.pdf
BibTex:
@INPROCEEDINGS{Lerner01,
AUTHOR = "Uri Lerner and Eran Segal and Daphne Koller",
TITLE = "Exact Inference in Networks with Discrete Children of Continuous Parents",
BOOKTITLE = "Proceedings of the Seventeenth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-01)",
PUBLISHER = "Morgan Kaufmann",
ADDRESS = "San Francisco, CA",
YEAR = "2001",
PAGES = "319--328"
}


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