Bayesian networks for expert systems: Theory and practical applications

Studies in Computational Intelligence

Volumn 281, Issue , 2010, Pages 547-578

  Wiegerinck, Wim ^a   Kappen, Bert ^b   Burgers, Willem ^a  

^a SNN Adaptive Intelligence   (Netherlands)

^b RADBOUD UNIVERSITY   (Netherlands)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 77950350653     PISSN: 1860949X     EISSN: None     Source Type: Book Series    
DOI: 10.1007/978-3-642-11688-9_20     Document Type: Article

References (27)

1. Balding, D., Nichols, R.: DNA profile match probability calculation: how to allow for population stratification, relatedness, database selection and single bands. Forensic Science International 64(2-3), 125-140 (1994)
2. Beinlich, I., Suermondt, H., Chavez, R., Cooper, G., et al.: The ALARM monitoring system: A case study with two probabilistic inference techniques for belief networks. In: Proceedings of the Second European Conference on Artificial Intelligence in Medicine, vol.256. Springer, Berlin (1989)
3. Bishop, C.: Pattern recognition and machine learning. Springer, Heidelberg (2006)
4. Brinkmann, B., Klintschar,M., Neuhuber, F., Hühne, J., Rolf, B.: Mutation rate in human microsatellites: influence of the structure and length of the tandem repeat. The American Journal of Human Genetics 62(6), 1408-1415 (1998)
5. Burgers, W., Wiegerinck, W., Kappen, H., Spalburg, M.: A Bayesian petrophysical decision support system for estimation of reservoir compositions (submitted)
6. Butler, J.: Forensic DNA typing: biology, technology, and genetics of STR markers. Academic Press, London (2005)
7. Castillo, E., Gutierrez, J.M., Hadi, A.S.: Expert Systems and Probabilistic Network Models. Springer, Heidelberg (1997)
8. Dawid, A., Mortera, J., Pascali, V.: Non-fatherhood or mutation? A probabilistic approach to parental exclusion in paternity testing. Forensic science international 124(1), 55-61 (2001)
9. Drábek, J.: Validation of software for calculating the likelihood ratio for parentage and kinship. Forensic Science International: Genetics 3(2), 112-118 (2009)
10. Duane, S., Kennedy, A., Pendleton, B., Roweth, D.: Hybrid Monte Carlo Algorithm. Phys. Lett. B 195, 216 (1987)
11. Fishelson, M., Geiger, D.: Exact genetic linkage computations for general pedigrees. Bioinformatics 198(Suppl. 1), S189-S198 (2002)
12. Friedman, N., Geiger, D., Lotner, N.: Likelihood computations using value abstraction. In: Proceedings of the Sixteenth Conference on Uncertainty in Artificial Intelligence, pp. 192-200. Morgan Kaufmann Publishers, San Francisco (2000)
13. Heckerman, D.: Probabilistic interpretations for mycin's certainty factors. In: Kanal, L., Lemmer, J. (eds.) Uncertainty in artificial intelligence, pp. 167-196. North-Holland, Amsterdam (1986)
14. Jensen, F.: An Introduction to Bayesian networks. UCL Press (1996)
15. Jordan, M.: Learning in graphical models. Kluwer Academic Publishers, Dordrecht (1998)
16. Lauritzen, S., Spiegelhalter, D.: Local computations with probabilities on graphical structures and their application to expert systems. Journal of the Royal Statistical Society. Series B (Methodological), 157-224 (1988)
17. MacKay, D.: Information theory, inference and learning algorithms. Cambridge University Press, Cambridge (2003)
18. Mahoney, S., Laskey, K.: Network engineering for complex belief networks. In: Proc. 12th Conf. on Uncertainty in Artificial Intelligence, pp. 389-396. Morgan Kaufmann, San Francisco (1996)
19. Metropolis, N., Rosenbluth, A., Rosenbluth, M., Teller, A., Teller, E.: Equation of state calculations by fast computing machines. The journal of chemical physics 21(6), 1087 (1953)
20. Pearl, J.: Probabilistic Reasoning in Intelligent systems: Networks of Plausible Inference. Morgan Kaufmann Publishers, Inc., San Francisco (1988)
21. Pradhan, M., Provan, G., Middleton, B., Henrion, M.: Knowledge engineering for large belief networks. In: Proc. Tenth Conf. on Uncertainty in Artificial Intelligence, pp. 484- 490 (1994)
22. Russell, S.,Norvig, P., Canny, J.,Malik, J., Edwards, D.: Artificial intelligence: amodern approach. Prentice Hall, Englewood Cliffs (2003)
23. Schlumberger: Log Interpretation Principles/Applications. Schlumberger Limited (1991)
24. Shortliffe, E., Buchanan, B.: A model of inexact reasoning in medicine. Mathematical Biosciences 23(3-4), 351-379 (1975)
25. Shwe, M., Middleton, B., Heckerman, D., Henrion, M., Lehman, H., Cooper, G.: Probabilistic Diagnosis Using a Reformulation of the Internist-1/ QMR Knowledge Base. Methods of Information in Medicine 30, 241-255 (1991)
26. Spalburg, M.: Bayesian uncertainty reduction for log evaluation. SPE International (2004); SPE88685
27. Takinawa,M., D'Ambrosio, B.:Multiplicative factorization of noisy-MAX. In: Proceedings of the 15th Conference on Uncertainty in Artificial Intelligence UAI 1999, pp. 622- 630 (1999)