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Summary: Decision Making by Credal Nets
Alessandro Antonucci
IDSIA
Lugano (Switzerland)
alessandro@idsia.ch
Cassio Polpo de Campos
IDSIA
Lugano (Switzerland)
cassio@idsia.ch
Abstract--Credal nets are probabilistic graphical models which
extend Bayesian nets to cope with sets of distributions. This
feature makes the model particularly suited for the implementa-
tion of classifiers and knowledge-based systems. When working
with sets of (instead of single) probability distributions, the
identification of the optimal option can be based on different
criteria, some of them eventually leading to multiple choices.
Yet, most of the inference algorithms for credal nets are designed
to compute only the bounds of the posterior probabilities. This
prevents some of the existing criteria from being used. To
overcome this limitation, we present two simple transformations
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