Abstract
This chapter explores the theory of Bayesian networks with particular reference to Maximum Entropy Formalism. A discussion of objective Bayesianism is given together with some brief remarks on applications.
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References
J. Pearl, Probabilistic reasoning in intelligent systems, in Networks of Plausible Inference (Morgan Kaufmann Publishers, San Francisco, 1988)
J. Pearl, Reverend Bayes on inference engines: a distributed hierarchical approach, in Proceedings, AAAI National Conference on AI (Pittsburgh, PA, 1982), pp. 133–136
D.E. Holmes, P.C. Rhodes, G.R. Garside, Efficient computation of marginal probabilities in multivalued Bayesian inverted multiway trees given incomplete information. Int. J. Intell. Syst. 14(6), 535–558 (1998)
D.E. Holmes, Independence in multivalued multiway causal trees, in Proceedings of the 19th Conference on Maximum Entropy and Bayesian Methods (Boise, Idaho. USA)
G.F. Cooper, The computational complexities of probabilistic inference using Bayesian belief networks. Artif. Intell. 42, 393–405 (1990)
R. Cowell, P. Dawid, S. Lauritzen, D. Spiegelhalter, Probabilistic Networks and Expert Systems (Springer, Berlin, 1999)
K.P. Murphy, Y. Weiss, M.I. Jordan, Loopy belief propagation for approximate inference: an empirical study, in Proceedings of the 15th Conference on Uncertainty in Artificial Intelligence (San Francisco, CA, 1999)
D.E. Holmes, P.C. Rhodes, Reasoning with incomplete information in a multivalued multiway causal tree using the maximum entropy formalism. Int. J. Intell. Syst. 13(9), 841–859 (1998)
C. Shannon, E and Weaver W (University of Illinois Press, The Mathematical Theory of Communication, 1948)
Jaynes, Prior Probabilities. IEE Trans. Syst. Sci. Cybern. SSC-4, 227–241 (1968)
P.C. Rhodes, G.R. Garside, The use of maximum entropy as a methodology for probabilistic reasoning.Knowl Based Syst 8(5), 249–258 (1995)
D.E. Holmes, Efficient Estimation of Missing Information in Multivalued Singly Connected Networks Using Maximum Entropy, in Maximum Entropy and Bayesian Methods. ed. by W. von der Linden, V. Dose, R. Fischer, R. Preuss (Kluwer Academic, Dordrecht, 1999), pp. 289–300
T. Verma, J. Pearl, Causal networks: semantics and expressiveness, in Proceedings of the 4th AAAI Workshop on Uncertainty in Artificial Intelligence (1988)
G.R. Garside, Conditional Independence Between Siblings in a causal Binary Tree with two-values Events (Research Report, Department of Computing, University of Bradford, UK, 1997)
D.E. Holmes, Independence in multivalued multiway causal trees. in Proceedings of the 19th Conference on Maximum Entropy and Bayesian Methods (2000)
G.R. Rhodes, Decision Support Systems: Theory and Practice (Alfred Waller, 1993)
B. De Fineeti, Theory of Probability (Wiley, New York, 1974)
M. Tribus, Rational Descriptions, Decisions and Designs (Pergamon, 1969)
D.E. Holmes, Why making objective Bayesian networks objectively Bayesian makes sense, in Causality and the Sciences, eds by Illari Russo and Williamson (2011), pp. 583–600
M. Correa, Comparison of Bayesian networks and artificial neural networks for quality detection in a machining process. Exp. Syst. Appl. 36(3), Part 2, 7270–7279 (2009)
A. Trojan Fenerich, M.T. Arns Steiner, J.C. Nievola, K. Borges Mendes, D.P. Tsutsumi, B. Samways dos Santos, Diagnosis of headaches types using artificial neural networks and bayesian networks. IEEE Latin Am Trans 18(01), 59–66 (2020). https://doi.org/10.1109/TLA.2020.9049462.
D.A. Roberts, S. Yaida, Based on research in collaboration with Boris Hanin, The Principles of Deep Learning Theory An Effective Theory Approach to Understanding Neural Networks due to be published by CUP in 2022
S. McLachlan, K. Dube, G.A. Hitman, N.E. Fenton, E. Kyrimi, Bayesian networks in healthcare: distribution by medical condition. Artif. Intell. Med. 107 (2020)
S. Chockalingam, W. Pieters, A. Teixeira, P. van Gelder, Bayesian network models in cyber security: a systematic review, in Secure IT Systems. Lecture Notes in Computer Science, vol. 10674, eds. by H. Lipmaa, A. Mitrokotsa, R. Matulevičius (Springer, Berlin, 2017)
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Holmes, D.E. (2022). Bayesian Networks: Theory and Philosophy. In: Virvou, M., Tsihrintzis, G.A., Jain, L.C. (eds) Advances in Selected Artificial Intelligence Areas. Learning and Analytics in Intelligent Systems, vol 24. Springer, Cham. https://doi.org/10.1007/978-3-030-93052-3_6
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DOI: https://doi.org/10.1007/978-3-030-93052-3_6
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