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Modeling unreliable observations in Bayesian networks by credal networks

Summary: Modeling unreliable observations
in Bayesian networks by credal networks
Alessandro Antonucci and Alberto Piatti
Dalle Molle Institute for Artificial Intelligence (IDSIA)
Galleria 2, Via Cantonale
CH6927 - Manno - Lugano (Switzerland)
Abstract. Bayesian networks are probabilistic graphical models widely
employed in AI for the implementation of knowledge-based systems.
Standard inference algorithms can update the beliefs about a variable of
interest in the network after the observation of some other variables. This
is usually achieved under the assumption that the observations could re-
veal the actual states of the variables in a fully reliable way. We propose
a procedure for a more general modeling of the observations, which al-
lows for updating beliefs in different situations, including various cases
of unreliable, incomplete, uncertain and also missing observations. This
is achieved by augmenting the original Bayesian network with a number
of auxiliary variables corresponding to the observations. For a flexible
modeling of the observational process, the quantification of the relations
between these auxiliary variables and those of the original Bayesian net-


Source: Antonucci, Alessandro - Istituto Dalle Molle di Studi sull'Intelligenza Artificiale (IDSIA)


Collections: Computer Technologies and Information Sciences