Structure-Based Categorisation of Bayesian Network Parameters

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10369)


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.



This research was supported by the Netherlands Organisation for Scientific Research (NWO).


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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Department of Information and Computing SciencesUtrecht UniversityUtrechtThe Netherlands

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