Analysing Sensitivity Data from Probabilistic Networks
This work addresses data overload issues for researchers analyzing probabilistic networks, but it appears incremental as it builds on existing sensitivity analysis methods.
The paper tackles the problem of overwhelming data from sensitivity analysis in probabilistic networks by proposing methods to extract relevant information, such as studying sensitivity function derivatives and using admissible deviation concepts, and illustrates these with a real-life oncology network.
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.