Uncertainty in Artificial Intelligence
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Toward a Market Model for Bayesian Inference
David Pennock, Michael Wellman
Abstract:
We present a methodology for representing probabilistic relationships in a general-equilibrium economic model. Specifically, we define a precise mapping from a Bayesian network with binary nodes to a market price system where consumers and producers trade in uncertain propositions. We demonstrate the correspondence between the equilibrium prices of goods in this economy and the probabilities represented by the Bayesian network. A computational market model such as this may provide a useful framework for investigations of belief aggregation, distributed probabilistic inference, resource allocation under uncertainty, and other problems of decentralized uncertainty.
Keywords: Computational market, belief aggregation, distributed probabilistic inference.
Pages: 405-413
PS Link: ftp://ftp.eecs.umich.edu/people/wellman/uai96pennock.ps.Z
PDF Link: /papers/96/p405-pennock.pdf
BibTex:
@INPROCEEDINGS{Pennock96,
AUTHOR = "David Pennock and Michael Wellman",
TITLE = "Toward a Market Model for Bayesian Inference",
BOOKTITLE = "Proceedings of the Twelfth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-96)",
PUBLISHER = "Morgan Kaufmann",
ADDRESS = "San Francisco, CA",
YEAR = "1996",
PAGES = "405--413"
}


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