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2008-2011
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SNF
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Unifying graphical models by credal networks: algorithms and applications
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Graph-based approaches to uncertainty and
statistics usually fall under the headline of
graphical models. There are many graphical
models. In this proposal we focus on Bayesian
networks, qualitative networks, influence
diagrams and decision nets, and, moreover, on
credal networks. All these models, but the last
one, are based on traditional, precise
probability, and they aim at computing
inferences or decisions under risk (i.e.,
determinate uncertainty). Credal networks are an
imprecise probability model: i.e., a credal net
can be regarded as a set of Bayesian nets. Our
recent research has provided basic tools to map
the following models into each other: credal
networks, decision networks, models of
incomplete information in Bayesian networks; the
same tools are expected to lead to bridging
credal and qualitative nets. This project is
concerned with exploring the mapping as well as
the opportunities originated by the
cross-fertilization among these fields of research.
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Sun Yi
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Coordinator
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Marco Zaffalon
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