Sensitivity Analysis in Bayesian Networks: From Single to Multiple
  Parameters
  Previous work on sensitivity analysis in Bayesian networks has focused on
single parameters, where the goal is to understand the sensitivity of queries
to single parameter changes, and to identify single parameter changes that
would enforce a certain query constraint. In this paper, we expand the work to
multiple parameters which may be in the CPT of a single variable, or the CPTs
of multiple variables. Not only do we identify the solution space of multiple
parameter changes that would be needed to enforce a query constraint, but we
also show how to find the optimal solution, that is, the one which disturbs the
current probability distribution the least (with respect to a specific measure
of disturbance). We characterize the computational complexity of our new
techniques and discuss their applications to developing and debugging Bayesian
networks, and to the problem of reasoning about the value (reliability) of new
information.
