Structure-Based Categorisation of Bayesian Network Parameters
Bayesian networks typically require thousands of probability para-meters for their specification, many of which are bound to be inaccurate. Know-ledge of the direction of change in an output probability of a network occasioned by changes in one or more of its parameters, i.e. the qualitative effect of parameter changes, has been shown to be useful both for parameter tuning and in pre-processing for inference in credal networks. In this paper we identify classes of parameter for which the qualitative effect on a given output of interest can be identified based upon graphical considerations.
- 1.Bolt, J.H., De Bock, J., Renooij, S.: Exploiting Bayesian network sensitivity functions for inference in credal networks. In: Proceedings of the 22nd European Conference on Artificial Intelligence, vol. 285, pp. 646–654 (2016)Google Scholar
- 6.Kjærulff, U., van der Gaag, L.C.: Making sensitivity analysis computationally efficient. In: Boutilier, C., Goldszmidt, M. (eds.) Proceedings of the Sixteenth Conference on Uncertainty in Artificial Intelligence, pp. 317–325. Morgan Kaufmann Publishers, San Francisco (2000)Google Scholar