Sensitivity Analysis in Bayesian Networks: From Single to Multiple Parameters
This work addresses the need for more robust sensitivity analysis in Bayesian networks, which is incremental but important for applications like network debugging and information reliability assessment.
The paper tackles the problem of extending sensitivity analysis in Bayesian networks from single to multiple parameters, identifying solution spaces for enforcing query constraints and finding optimal solutions that minimally disturb the current probability distribution.
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.