Uncertainty in Artificial Intelligence
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A New Characterization of Probabilities in Bayesian Networks
Lenhart Schubert
Abstract:
We characterize probabilities in Bayesian networks in terms of algebraic expressions called quasi-probabilities. These are arrived at by casting Bayesian networks as noisy AND-OR-NOT networks, and viewing the subnetworks that lead to a node as arguments for or against a node. Quasi-probabilities are in a sense the "natural" algebra of Bayesian networks: we can easily compute the marginal quasi-probability of any node recursively, in a compact form; and we can obtain the joint quasi-probability of any set of nodes by multiplying their marginals (using an idempotent product operator). Quasi-probabilities are easily manipulated to improve the efficiency of probabilistic inference. They also turn out to be representable as square-wave pulse trains, and joint and marginal distributions can be computed by multiplication and complementation of pulse trains.
Keywords:
Pages: 495-503
PS Link:
PDF Link: /papers/04/p495-schubert.pdf
BibTex:
@INPROCEEDINGS{Schubert04,
AUTHOR = "Lenhart Schubert ",
TITLE = "A New Characterization of Probabilities in Bayesian Networks",
BOOKTITLE = "Proceedings of the Twentieth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-04)",
PUBLISHER = "AUAI Press",
ADDRESS = "Arlington, Virginia",
YEAR = "2004",
PAGES = "495--503"
}


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