Uncertainty in Artificial Intelligence
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Inference for Multiplicative Models
Ydo Wexler, Christopher Meek
Abstract:
The paper introduces a generalization for known probabilistic models such as log-linear and graphical models, called here multiplicative models. These models, that express probabilities via product of parameters are shown to capture multiple forms of contextual independence between variables, including decision graphs and noisy-OR functions. An inference algorithm for multiplicative models is provided and its correctness is proved. The complexity analysis of the inference algorithm uses a more refined parameter than the tree-width of the underlying graph, and shows the computational cost does not exceed that of the variable elimination algorithm in graphical models. The paper ends with examples where using the new models and algorithm is computationally beneficial.
Keywords: null
Pages: 595-602
PS Link:
PDF Link: /papers/08/p595-wexler.pdf
BibTex:
@INPROCEEDINGS{Wexler08,
AUTHOR = "Ydo Wexler and Christopher Meek",
TITLE = "Inference for Multiplicative Models",
BOOKTITLE = "Proceedings of the Twenty-Fourth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-08)",
PUBLISHER = "AUAI Press",
ADDRESS = "Corvallis, Oregon",
YEAR = "2008",
PAGES = "595--602"
}


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