Uncertain Bayesian Networks: Learning from Incomplete Data. (arXiv:2208.04221v1 [stat.ML])

When the historical data are limited, the conditional probabilities
associated with the nodes of Bayesian networks are uncertain and can be
empirically estimated. Second order estimation methods provide a framework for
both estimating the probabilities and quantifying the uncertainty in these
estimates. We refer to these cases as uncer tain or second-order Bayesian
networks. When such data are complete, i.e., all variable values are observed
for each instantiation, the conditional probabilities are known to be
Dirichlet-distributed. This paper improves the …

arxiv bayesian data incomplete data learning ml networks uncertain