Uncertain Bayesian Networks: Learning from Incomplete Data

@article{Hougen2021UncertainBN,
  title={Uncertain Bayesian Networks: Learning from Incomplete Data},
  author={Conrad D. Hougen and Lance M. Kaplan and F. Cerutti and Alfred O. Hero},
  journal={2021 IEEE 31st International Workshop on Machine Learning for Signal Processing (MLSP)},
  year={2021},
  pages={1-6}
}
  • Conrad D. HougenL. Kaplan A. Hero
  • Published 25 October 2021
  • Computer Science
  • 2021 IEEE 31st International Workshop on Machine Learning for Signal Processing (MLSP)
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 uncertain or second-order Bayesian networks. When such data are complete, i.e., all variable values are observed for each instantiation, the conditional probabilities… 
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