Generalised Co-variation for Sensitivity Analysis in Bayesian Networks

Upon varying parameters in a sensitivity analysis of a Bayesian network, the standard approach is to co-vary the parameters from the same conditional distribution such that their proportions remain the same. Alternative co-variation schemes are, however, possible. We theoretically investigate the effects of using alternative co-variation schemes on the so-called sensitivity function, and conclude that its general form remains the same under any linear co-variation scheme. In addition, we generalise the CD-distance for bounding global belief change, and prove a tight lower bound on this distance for parameter changes in single conditional probability tables.