Lower Bound Bayesian Networks - An Efficient Inference of Lower Bounds
  on Probability Distributions in Bayesian Networks
  We present a new method to propagate lower bounds on conditional probability
distributions in conventional Bayesian networks. Our method guarantees to
provide outer approximations of the exact lower bounds. A key advantage is that
we can use any available algorithms and tools for Bayesian networks in order to
represent and infer lower bounds. This new method yields results that are
provable exact for trees with binary variables, and results which are
competitive to existing approximations in credal networks for all other network
structures. Our method is not limited to a specific kind of network structure.
Basically, it is also not restricted to a specific kind of inference, but we
restrict our analysis to prognostic inference in this article. The
computational complexity is superior to that of other existing approaches.
