Challenges for bayesian network learning in a flood damage assessment application

Safety, Reliability, Risk and Life-Cycle Performance of Structures and Infrastructures - Proceedings of the 11th International Conference on Structural Safety and Reliability, ICOSSAR 2013

Volumn , Issue , 2013, Pages 3123-3130

  Vogel, K ^a   Riggelsen, C ^a   Scherbaum, F ^a   Schroter K ^b   Kreibich, H ^b   Merz, B ^b  

^a UNIVERSITY OF POTSDAM   (Germany)

^b GFZ GeoForschungszentrum Potsdam ^*  (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

BAYESIAN NETWORK LEARNING; BUILDING CHARACTERISTICS; CONTINUOUS VARIABLES; INCOMPLETE OBSERVATION; LEARNING BAYESIAN NETWORKS; MAXIMUM A POSTERIORI; PREDICTIVE DISTRIBUTIONS; SOCIO-ECONOMIC FACTOR;

ALGORITHMS; AUTOCORRELATION; DAMAGE DETECTION; FLOOD DAMAGE; RELIABILITY; SAFETY ENGINEERING;

BAYESIAN NETWORKS;

EID: 84892406694     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (32)

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16. Vogel, K., Riggelsen, C., Merz, B., Kreibich, H. & Scherbaum, F. 2012b. Flood Damage and Influencing Factors: A Bayesian Network Perspective. Proceedings of the 6th European Workshop on Probabilistic Graphical Models, 347-354.
17. Castelo, R. & Kocka, T. 2003. On inclusion-driven learning of Bayesian networks. The Journal of Machine Learning Research 4, pp. 527-574.
18. Elmer, F., Thieken, A.H., Pech, I. & Kreibich, H. 2010. Influence of flood frequency on residential building losses. Natural Hazards and Earth System Sciences 10, pp. 2145-2159.
19. Friedman, N. 1997. Learning belief networks in the presence of missing values and hidden variables. In Fourteenth International Conference on Machine Learning, 125-133.
20. Friedman, N. 1998. The Bayesian structural EM Algorithm. In Fourteenth conference on Uncertainty in artificial intelligence, 129-138.
21. Langseth, H., Nielsen, T.D., Rumi, R. & Salmerón, A. 2009. Maximum Likelihood Learning of Conditional MTE Distributions. Symbolic and Quantitative Approaches to Reasoning with Uncertainty, 240-251.
22. Langseth, H., Nielsen, T.D., Rumi, R. & Salmerón, A. 2010. Parameter estimation and model selection for mixtures of truncated exponentials. International Journal of Approximate Reasoning 51(5), 485-498.
23. Little, R. & Rubin, D. 1987. Statistical analysis with missing data, Volume 4. NewYork:Wiley.
24. Merz, B., Kreibich, H., Schwarze, R. &Thieken, A.H. 2010. Assessment of economic flood damage. Nat. Hazards and Earth Syst. Sci. 10, 1697-1724.
25. Monti, S. & Cooper, G.F. 1998. A multivariate discretization method for learning Bayesian networks from mixed data. In Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence UAI'98, 404-413.
26. Moral, S., Rumí, R. & Salmerón, A. 2001. Mixtures of Truncated Exponentials in Hybrid Bayesian Networks. In S. Benferhat and P. Besnard (Eds.), Symbolic and Quantitative Approaches to Reasoning with Uncertainty, 156-167.
27. Riggelsen, C. 2006. Learning Bayesian Networks from Incomplete Data: An Efficient Method for Generating Approximate Predictive Distributions. In SIAM International conf. on data mining, 130-140.
28. Riggelsen, C. 2008. Learning Bayesian Networks: A MAP Criterion for Joint Selection of Model Structure and Parameter. In ICDM, 2008 Eighth IEEE International Conference on Data Mining, 522-529.
29. Tanner, M.&Wong, W. 1987.The calculation of posterior distributions by data augmentation. Journal of the American statistical Association 82(398), 528-540.
30. Thieken, A.H., Müller, M., Kreibich, H. & Merz, B. 2005. Flood damage and influencing factors: New insights from the August 2002 flood in Germany. Water resources research 41.
31. Vogel, K., Riggelsen, C., Kuehn, N. & Scherbaum, F. 2012a. Graphical Models as Surrogates for Complex Ground Motion Models. Proceedings of the 11th International Conference on Artificial Intelligence and Soft Computing.
32. Vogel, K., Riggelsen, C., Merz, B., Kreibich, H. & Scherbaum, F. 2012b. Flood Damage and Influencing Factors: A Bayesian Network Perspective. Proceedings of the 6th European Workshop on Probabilistic Graphical Models, 347-354.