Abstract
Focussed Bayesian fusion reduces high computational costs caused by Bayesian fusion by restricting the range of the Properties of Interest which specify the structure of the desired information on its most task relevant part. Within this publication, it is concisely explained how Bayesian theory and the theory of statistical evidence can be combined to derive meaningful focussed Bayesian models and to rate the validity of a focussed Bayesian analysis quantitatively. Earlier results with regard to this topic will be further developed and exemplified.
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References
Asuncion, A., Newman, D.J.: UCI Machine Learning Repository. Irvine: University of California, School of Information and Computer Science (2007), http://www.ics.uci.edu/~mlearn/MLRepository.html
Aitken, C.G.G., Taroni, F.: Statistics and the Evaluation of Evidence for Forensic Scientists. Wiley, Chichester (2004)
Bartelborth, T.: Wofür sprechen die Daten? Journal for General Philosophy of Science 35(1), 13–40 (2004)
Bernardo, J.M., Smith, A.F.M.: Bayesian Theory. Wiley, Chichester (2004)
Beyerer, J., Heizmann, M., Sander, J.: Fuselets – an Agent Based Architecture for Fusion of Heterogeneous Information and Data. In: Dasarathy, B.V. (ed.) Proceedings of SPIE, Multisensor, Multisource Information Fusion: Architectures, Algorithms and Applications 2006, vol. 6242. SPIE, Bellingham (2006)
Beyerer, J., Heizmann, M., Sander, J., Gheta, I.: Bayesian Methods for Image Fusion. In: Stathaki, T. (ed.) Image Fusion: Algorithms and Applications. Academic Press, Amsterdam (2008)
Blume, J.D.: Likelihood Methods for Measuring Statistical Evidence. Statistics in Medicine 21(17), 2563–2599 (2002)
Dempster, A.P.: The Direct Use of Likelihood for Significance Testing. Statistics and Computing 7(4), 247–252 (1997)
Domingos, P., Pazzani, M.: On the Optimality of the Simple Bayesian Classifier under Zero-One Loss. Mach. Learn. 29(2-3), 103–130 (1997)
Duda, R.O., Hart, P.E., Stork, D.G.: Pattern Classification. Wiley, New York (2001)
Edwards, A.W.F.: Likelihood. Cambridge University Press, London (1972)
Goodman, S.N.: Toward Evidence-Based Medical Statistics. 2: the Bayes Factor. Ann. Intern. Med. 130(12), 1005–1013 (1999)
Ekdahl, M., Koski, T.: Bounds for the Loss in Probability of Correct Classification under Model Based Approximation. J. Mach. Learn. Res. 7, 2449–2480 (2006)
Hacking, I.: Logic of Statistical Inference. Cambridge University Press, New York (1965)
Lindley, D.V.: The Probability Approach to the Treatment of Uncertainty in Artificial Intelligence and Expert Systems. Statistical Science 2(2), 3–44 (1987)
Royall, R.M.: On the Probability of Observing Misleading Statistical Evidence. Journal of the American Statistical Association 95(451), 760–768 (2000)
Royall, R.M.: Statistical Evidence: a Likelihood Paradigm. Chapman & Hall, London (1997)
Sander, J., Beyerer, J.: Decreased Complexity and Increased Problem Specificity of Bayesian Fusion by Local Approaches. In: Proceedings of the 11th International Conference on Information Fusion, Fusion 2008, pp. 1035–1042. IEEE Press, Los Alamitos (2008)
Sander, J., Heizmann, M., Goussev, I., Beyerer, J.: A Local Approach for Focussed Bayesian Fusion. In: Dasarathy, B.V. (ed.) Proceedings of SPIE, Multisensor, Multisource Information Fusion: Architectures, Algorithms and Applications 2009, vol. 7345. SPIE, Bellingham (2009)
Sander, J., Heizmann, M., Goussev, I., Beyerer, J.: Global Evaluation of Focussed Bayesian Fusion. In: Braun, J.J. (ed.) Proceedings of SPIE, Multisensor, Multisource Information Fusion: Architectures, Algorithms and Applications 2010, vol. 7710. SPIE, Bellingham (2010)
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Sander, J., Krieger, J., Beyerer, J. (2010). The Importance of Statistical Evidence for Focussed Bayesian Fusion. In: Dillmann, R., Beyerer, J., Hanebeck, U.D., Schultz, T. (eds) KI 2010: Advances in Artificial Intelligence. KI 2010. Lecture Notes in Computer Science(), vol 6359. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-16111-7_34
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DOI: https://doi.org/10.1007/978-3-642-16111-7_34
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