Unity Smoothing for Handling Inconsistent Evidence in Bayesian Networks and Unity Propagation for Faster Inference. (arXiv:2201.08659v1 [cs.LG])

We propose Unity Smoothing (US) for handling inconsistencies between a
Bayesian network model and new unseen observations. We show that prediction
accuracy, using the junction tree algorithm with US is comparable to that of
Laplace smoothing. Moreover, in applications were sparsity of the data
structures is utilized, US outperforms Laplace smoothing in terms of memory
usage. Furthermore, we detail how to avoid redundant calculations that must
otherwise be performed during the message passing scheme in the junction tree
algorithm which …

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