Uncertainty in Artificial Intelligence
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A computational scheme for Reasoning in Dynamic Probabilistic Networks
Uffe Kjærulff
Abstract:
A computational scheme for reasoning about dynamic systems using (causal) probabilistic networks is presented. The scheme is based on the framework of Lauritzen and Spiegelhalter (1988), and may be viewed as a generalization of the inference methods of classical time-series analysis in the sense that it allows description of non-linear, multivariate dynamic systems with complex conditional independence structures. Further, the scheme provides a method for efficient backward smoothing and possibilities for efficient, approximate forecasting methods. The scheme has been implemented on top of the HUGIN shell.
Keywords:
Pages: 121-129
PS Link:
PDF Link: /papers/92/p121-kjaerulff.pdf
BibTex:
@INPROCEEDINGS{Kjærulff92,
AUTHOR = "Uffe Kjærulff ",
TITLE = "A computational scheme for Reasoning in Dynamic Probabilistic Networks",
BOOKTITLE = "Proceedings of the Eighth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-92)",
PUBLISHER = "Morgan Kaufmann",
ADDRESS = "San Mateo, CA",
YEAR = "1992",
PAGES = "121--129"
}


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