Structure-based categorisation of Bayesian network parameters
Publication date
2017-06-15
Authors
Bolt, J.H.
Renooij, Silja
Editors
Antonucci, Alessandro
Cholvy, Laurence
Papini, Odile
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Supervisors
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Part of book
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Abstract
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.
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Citation
Bolt, J H & Renooij, S 2017, Structure-based categorisation of Bayesian network parameters. in A Antonucci, L Cholvy & O Papini (eds), Symbolic and Quantitative Approaches to Reasoning with Uncertainty : 14th European Conference, ECSQARU 2017, Lugano, Switzerland, July 10–14, 2017, Proceedings. Lecture Notes in Computer Science , vol. 10369, Springer, pp. 83-92, Fourteenth European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty, Lugano, Switzerland, 11/07/17. https://doi.org/10.1007/978-3-319-61581-3_8, conference