Uncertainty in Artificial Intelligence
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Learning Bayesian Network Parameters with Prior Knowledge about Context-Specific Qualitative Influences
Ad Feelders, Linda van der Gaag
Abstract:
We present a method for learning the parameters of a Bayesian network with prior knowledge about the signs of influences between variables. Our method accommodates not just the standard signs, but provides for context-specific signs as well. We show how the various signs translate into order constraints on the network parameters and how isotonic regression can be used to compute order-constrained estimates from the available data. Our experimental results show that taking prior knowledge about the signs of influences into account leads to an improved fit of the true distribution, especially when only a small sample of data is available. Moreover, the computed estimates are guaranteed to be consistent with the specified signs, thereby resulting in a network that is more likely to be accepted by experts in its domain of application.
Keywords:
Pages: 193-200
PS Link:
PDF Link: /papers/05/p193-feelders.pdf
BibTex:
@INPROCEEDINGS{Feelders05,
AUTHOR = "Ad Feelders and Linda van der Gaag",
TITLE = "Learning Bayesian Network Parameters with Prior Knowledge about Context-Specific Qualitative Influences",
BOOKTITLE = "Proceedings of the Twenty-First Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-05)",
PUBLISHER = "AUAI Press",
ADDRESS = "Arlington, Virginia",
YEAR = "2005",
PAGES = "193--200"
}


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