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...

• Type: Conference
• Year: 2009
• Where: SUM
• Authors: Alessandro Antonucci, Alberto Piatti
• Keywords: Auxiliary Variables, Bayesian Network, Information Management, Original Bayesian Network, SUM 2009