Abstract
For many inference tasks in Bayesian networks, computational efforts can be restricted to a relevant part of the network. Researchers have studied the relevance of a network’s variables and parameter probabilities for such tasks as sensitivity analysis and probabilistic inference in general, and identified relevant sets of variables by graphical considerations. In this paper we study relevance of the evidence variables of a network for such tasks as evidence sensitivity analysis and diagnostic test selection, and identify sets of variables on which computational efforts can focus. We relate the newly identified sets of relevant variables to previously established relevance sets and address their computation compared to these sets. We thereby paint an overall picture of the relevance of various variable sets for answering questions concerning inference and analysis in Bayesian network applications.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Baker, M., Boult, T.: Pruning Bayesian networks for efficient computation. In: Bonissone, P.P., Henrion, M., Kanal, L.N., Lemmer, J.F. (eds.) Uncertainty in Artificial Intelligence 6, pp. 225–232. Elsevier Science, Amsterdam (1991)
Coupé, V.M.H., van der Gaag, L.C.: Properties of sensitivity analysis of Bayesian belief networks. Ann. Math. Artif. Intell. 36(4), 323–356 (2002)
Druzdzel, M., Suermondt, H.: Relevance in probabilistic models: “backyards” in a “small world”. In: Working Notes of the AAAI 1994 Fall Symposium Series: Relevance, pp. 60–63 (1994)
Jensen, F.V., Aldenryd, S.H., Jensen, K.B.: Sensitivity analysis in Bayesian networks. In: Froidevaux, C., Kohlas, J. (eds.) ECSQARU 1995. LNCS, vol. 946, pp. 243–250. Springer, Heidelberg (1995)
Jensen, F.V., Nielsen, T.D.: Bayesian Networks and Decision Graphs. Springer, New York (2007)
Meekes, M.: Sensitivity to evidence in probabilistic networks. Master’s thesis, Faculty of Science, Utrecht University (2013)
Park, J., Darwiche, A.: Complexity results and approximation strategies for MAP explanations. J. Artif. Intell. Res. 21, 101–133 (2004)
Pearl, J.: Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. Morgan Kaufmann Publishers, San Mateo (1988)
Shachter, R.: Bayes-ball: The rational pastime (for determining irrelevance and requisite information in belief networks and influence diagrams). In: Cooper, G., Moral, S. (eds.) Proceedings of the Fourteenth Conference on Uncertainty in Artificial Intelligence, pp. 480–487. Morgan Kaufmann, San Francisco (1998)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Meekes, M., Renooij, S., van der Gaag, L.C. (2015). Relevance of Evidence in Bayesian Networks. In: Destercke, S., Denoeux, T. (eds) Symbolic and Quantitative Approaches to Reasoning with Uncertainty. ECSQARU 2015. Lecture Notes in Computer Science(), vol 9161. Springer, Cham. https://doi.org/10.1007/978-3-319-20807-7_33
Download citation
DOI: https://doi.org/10.1007/978-3-319-20807-7_33
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-20806-0
Online ISBN: 978-3-319-20807-7
eBook Packages: Computer ScienceComputer Science (R0)