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We propose Unity Smoothing (US) for handling inconsistencies between aBayesian network model and new unseen observations. We show that predictionaccuracy, using the junction tree algorithm with US is comparable to that ofLaplace smoothing. Moreover, in applications were sparsity of the datastructures is utilized, US outperforms Laplace smoothing in terms of memoryusage. Furthermore, we detail how to avoid redundant calculations that mustotherwise be performed during the message passing scheme in the junction treealgorithm which we refer to as Unity Propagation (UP). Experimental resultsshows that it is always faster to exploit UP on top of theLauritzen-Spigelhalter message passing scheme for the junction tree algorithm.

Unity Smoothing for Handling Inconsistent Evidence in Bayesian Networks and Unity Propagation for Faster Inference