Abstract
An algorithm, called Semantics in Inference (SI) has been proposed recently for determining semantics of the intermediate factors constructed during exact inference in discrete Bayesian networks. In this paper, we establish the soundness and completeness of SI. We also suggest an alternative version of SI, one that is perhaps more intuitive as it is a simpler graphical approach to deciding semantics.
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References
Butz, C.J., Yan, W.: The semantics of intermediate CPTs in variable elimination. In: Proc. of Fifth European Workshop on Probabilistic Graphical Models, pp. 41–48 (2010)
Butz, C.J., Yan, W., Madsen, A.L.: d-Separation: strong completeness of semantics of intermediate CPTs in variable elimination. Submitted to the Canadian Conference on Artificial Intelligence, CAI (2013)
Butz, C.J., Yan, W., Lingras, P., Yao, Y.Y.: The CPT structure of variable elimination in discrete Bayesian networks. In: Ras, Z.W., Tsay, L.-S. (eds.) Advances in Intelligent Information Systems. SCI, vol. 265, pp. 245–257. Springer, Heidelberg (2010)
Castillo, E., Gutiérrez, J., Hadi, A.: Expert Systems and Probabilistic Network Models. Springer, New York (1997)
Cormen, T.H., Leiserson, C.E., Rivest, R.L., Stein, C.: Introduction to Algorithms. MIT Press, Cambridge (2009)
Darwiche, A.: Modeling and Reasoning with Bayesian Networks. Cambridge University Press, New York (2009)
Kjærulff, U.B., Madsen, A.L.: Bayesian Networks and Influence Diagrams, 2nd edn. Springer, New York (2013)
Koller, D., Friedman, N.: Probabilistic Graphical Models: Principles and Techniques. MIT Press, Cambridge (2009)
Madsen, A.L.: A differential semantics of Lazy AR Propagation. In: Proc. of Twenty-First Annual Conference on Uncertainty in Artificial Intelligence (UAI), pp. 364–371 (1995)
Meek, C.: Strong completeness and faithfulness in Bayesian networks. In: Proc. of Eleventh Annual Conference on Uncertainty in Artificial Intelligence (UAI), pp. 411–418 (1995)
Pearl, J.: Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. Morgan Kaufmann, San Francisco (1988)
Shafer, G.: Probabilistic Expert Systems. SIAM, Philadelphia (1996)
Verma, T., Pearl, J.: Causal networks: semantics and expressiveness. In: Proc. of Fourth Annual Conference on Uncertainty in Artificial Intelligence (UAI), pp. 352–359 (1998)
Zhang, N.L., Poole, D.: A simple approach to Bayesian network computations. In: Proc. of Canadian Conference on Artificial Intelligence (CAI), pp. 171–178 (1994)
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Butz, C.J., Yan, W., Madsen, A.L. (2013). On Semantics of Inference in Bayesian Networks. In: van der Gaag, L.C. (eds) Symbolic and Quantitative Approaches to Reasoning with Uncertainty. ECSQARU 2013. Lecture Notes in Computer Science(), vol 7958. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-39091-3_7
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DOI: https://doi.org/10.1007/978-3-642-39091-3_7
Publisher Name: Springer, Berlin, Heidelberg
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