Abstract
For systems based on Bayesian networks, evidence is used to compute posterior probabilities for some hypotheses. Sensitivity analysis is concerned with questions on how sensitive the conclusion is to the evidence provided. After the basic definitions and an example we conclude that the heart of sensitivity analysis is to compute probabilities for the hypotheses given various subsets of the evidence. We show how some of these probabilities come out as a side-effect of the HUGIN propagation method. Through a modification of the HUGIN method even more of the probabilities are achieved. Finally, we give methods for answering “what if”-questions, for determining the set of crucial findings, and determining minimal sufficient sets of findings.
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References
Dawid, A. (1992). Applications of a general propagation algorithm for probabilistic expert system, Statistics and Computing 2: 25–36.
Geiger, D., Verma, T. & Pearl, J. (1990). d-separation: Prom theorems to algorithms, in M. Henrion, R. Shachter, L. Kanal & J. Lemmer (eds), Uncertainty in Artificial Intelligence 5, Elsevier Science Publishers.
Henrion, M. & Druzdel, M. (1990). Qualitative propagation and scenario-based approaches to explanation of probabilistic reasoning, Proceedings of the 6th Conference on Uncertainty in Artificial Intelligence.
Jensen, F. V. (1995). Cautious propagation in Bayesian networks, Technical Report R-95-2004, Aalborg University, Dept. of Mathematics and Computer Science.
Jensen, F. V., Chamberlain, B., Nordahl, T. & Jensen, F. (1991). Analysis in HUGIN of data conflict, in N.-H. Bonnisone et al. (ed.), Uncertainty in Artificial Intelligence 6, pp. 519–528.
Jensen, F. V., Lauritzen, S. L. & Olesen, K. G. (1990). Bayesian updating in causal probabilistic networks by local computations, Computational Statistics Quarterly 4: 269–282.
Madigan, D. & Mosurski, K. (1993). Explanation in belief networks, Technical report, University of Washington, US and Trinity College, Dublin, Ireland.
Shafer, G. & Shenoy, P. (1990). Probability propagation, Annals of Mathematics and Artificial Intelligence 2: 327–352.
Spiegelhalter, D. J. & Knill-Jones, R. P. (1984). Statistical and knowledge-based approaches to clinical decision-support systems, Journal of the Royal Statistical Society, Series A pp. 35–77.
Suermondt, H. J. (1992). Explanation in Bayesian Belief Networks, PhD thesis, Knowledge Systems Laboratory, Medical Computer Science, Stanford University, California, Stanford, California 94305. Report No. STAN-CS-92-1417.
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© 1995 Springer-Verlag Berlin Heidelberg
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Jensen, F.V., Aldenryd, S.H., Jensen, K.B. (1995). Sensitivity analysis in Bayesian networks. In: Froidevaux, C., Kohlas, J. (eds) Symbolic and Quantitative Approaches to Reasoning and Uncertainty. ECSQARU 1995. Lecture Notes in Computer Science, vol 946. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-60112-0_28
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DOI: https://doi.org/10.1007/3-540-60112-0_28
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