Abstract
In Chap. 23 we discussed graphical models as tools for structuring uncertain knowledge about high-dimensional domains. More precisely, we introduced Bayes networks, which are based on directed graphs and conditional distributions, and Markov networks, which refer to undirected graphs and marginal distributions or factor potentials. Further, we presented an evidence propagation method in Chap. 24 with which it is possible to condition the probability distribution represented by a graphical model on given evidence, i.e., on observed values for some of the variables, a reasoning process that is also called focusing.
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References
P. Gärdenfors, Knowledge in Flux: Modeling the Dynamics of Epistemic States (MIT Press, Cambridge, 1988)
J. Gebhardt, The Revision Operator and the Treatment of Inconsistent Stipulations of Item Rates. Project EPL: Internal Report 9 (ISC Gebhardt and Volkswagen Group, K-DOB-11, Wolfsburg, Germany, 2001)
J. Gebhardt, C. Borgelt, R. Kruse, H. Detmer, Knowledge Revision in Markov Networks. Mathw. Soft Comput. 11(2–3), 93–107, (2004). (University of Granada, Granada, Spain, 2004)
J. Gebhardt, H. Detmer, A.L. Madsen, Predicting Parts Demand in the Automotive Industry—An Application of Probabilistic Graphical Models, in Proceedings of the Bayesian Modelling Applications Workshop at International Joint Conference on Uncertainty in Artificial Intelligence, (UAI 2003) (Acapulco, Mexico, 2003)
J. Gebhardt, R. Kruse, Knowledge-Based Operations, for Graphical Models in Planning, in Proceedings of the European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty (ECSQARU, Barcelona, Spain), LNAI vol. 3571 (Springer-verlag, Berlin, Germany, 2005), pp. 3–14
J. Gebhardt, A. Klose, H. Detmer, F. Rügheimer, R. Kruse, Graphical models for industrial planning on complex domains, in, D. Della Riccia, D. Dubois, R. Kruse, H.-J. Lenz (ed.) Decision Theory and Multi-Agent Planning, CISM Courses and Lectures vol. 482 (Springer-Verlag, Berlin, Germany, 2006), pp. 131–143
Hugin Expert. HUGIN—Bayesian network software (Aalborg, Denmark, 1989–2016) http://www.hugin.com
F.V. Jensen, An Introduction to Bayesian Networks (UCL Press, London, United Kingdom, 1996)
S.L. Lauritzen, D.J. Spiegelhalter, Local computations with probabilities on graphical structures and their application to expert systems. J. R. Stat. Soc. Ser. B 2(50), 157–224 (1988). (Blackwell, Oxford, United Kingdom, 1988)
Norsys Software Corp. NETICA—Bayesian network software (Vancouver, BC, Canada, 1995–2016). http://www.norsys.com/
F. Schmidt, J. Wendler, J. Gebhardt, R. Kruse, Handling Inconsistencies in the Revision of Probability Distributions, HAIS13, Lecture Notes in Computer Science, vol. 8073 (Springer, Heidelberg, 2013), pp. 598–607
F. Schmidt, J. Gebhardt, R. Kruse, Handling Revision Inconsistencies: Towards Better Explanations, in Symbolic and Quantitative Approaches to Reasoning with Uncertainty (Springer International Publishing, Heidelberg, 2015a), pp. 257–266
F. Schmidt, J. Gebhardt, R. Kruse, Handling revision inconsistencies: creating useful explanations, in Proceedings of the HICSS 2015, ed. by S. Destercke, T. Denoeux (Koloa, Kauai, HI, USA, 2015b)
J. Whittaker, Graphical Models in Applied Multivariate Statistics (Whiley, Chichester, United Kingdom, 1990)
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Kruse, R., Borgelt, C., Braune, C., Mostaghim, S., Steinbrecher, M. (2016). Belief Revision. In: Computational Intelligence. Texts in Computer Science. Springer, London. https://doi.org/10.1007/978-1-4471-7296-3_26
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DOI: https://doi.org/10.1007/978-1-4471-7296-3_26
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