Analysing Sensitivity Data from Probabilistic Networks
  With the advance of efficient analytical methods for sensitivity analysis
ofprobabilistic networks, the interest in the sensitivities revealed by
real-life networks is rekindled. As the amount of data resulting from a
sensitivity analysis of even a moderately-sized network is alreadyoverwhelming,
methods for extracting relevant information are called for. One such methodis
to study the derivative of the sensitivity functions yielded for a network's
parameters. We further propose to build upon the concept of admissible
deviation, that is, the extent to which a parameter can deviate from the true
value without inducing a change in the most likely outcome. We illustrate these
concepts by means of a sensitivity analysis of a real-life probabilistic
network in oncology.
