Modeling Unreliable Observations in Bayesian Networks by Credal Networks

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Modeling Unreliable Observations in Bayesian Networks by Credal Networks
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 reveal the actual states of the variables in a fully reliable way. We propose a procedure for a more general modeling of the observations, which allows 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 network is done by credal sets, i.e., convex sets of probability mass f...
Alessandro Antonucci, Alberto Piatti
Added 27 May 2010
Updated 27 May 2010
Type Conference
Year 2009
Where SUM
Authors Alessandro Antonucci, Alberto Piatti
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