SAFECOMP 2014: Computer Safety, Reliability, and Security pp 328-338 | Cite as
Probabilistic Inference in the Physical Simulation of Interdependent Critical Infrastructure Systems
Conference paper
Abstract
One of the main tasks that can be performed with a Bayesian Network (BN) is the probabilistic inference of unobserved values given evidence. Recently, a framework for physical simulation of critical infrastructures was introduced, accounting for interdependencies and uncertainty; this framework includes the modeling of the interconnected components of a critical infrastructure network as a BN. In this paper we address the problem of the triangulation of the resulting BN, that is the first step in many exact inference algorithms.
Keywords
Bayesian Network Seismic Hazard Seismic Source Critical Infrastructure Physical Simulation
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
Preview
Unable to display preview. Download preview PDF.
References
- 1.Asprone, D., Cavallaro, M., Latora, V., Manfredi, G., Nicosia, V.: City ecosystem resilience analysis in case of disasters. In: Computer Aided Civil & Infrastructure Engineering (2013) (submitted)Google Scholar
- 2.Bensi, M.T., Der Kiureghian, A., Straub, D.: A Bayesian network methodology for infrastructure seismic risk assessment and decision support. Pacific Earthquake Engineering Research Center (2011)Google Scholar
- 3.Cavalieri, F., Franchin, P., Gehl, P., Khazai, B.: Quantitative assessment of social losses based on physical damage and interaction with infrastructural systems. Earthquake Engineering & Structural Dynamics 41(11), 1569–1589 (2012)CrossRefGoogle Scholar
- 4.Franchin, P.: A Computational Framework for Systemic Seismic Risk Analysis of Civil Infrastructural Systems. In: Pitilakis, K., et al. (eds.) SYNER-G: Systemic Seismic Vulnerability and Risk Assessment of Complex Urban, Utility, Lifeline Systems and Critical Facilities. Geotechnical, Geological and Earthquake Engineering, vol. 31, pp. 23–56. Springer Science+Business Media Dordrecht (2014), doi:10.1007/978-94-017-8835-9_2Google Scholar
- 5.Franchin, P., Cavalieri, F.: A framework for physical simulation of critical infrastructures, accounting for interdependencies and uncertainty. In: 11th International Conference on Structural Safety & Reliability, ICOSSAR 2013, New York, NY, USA, June 16-20 (2013)Google Scholar
- 6.Jayaram, N., Baker, J.W.: Efficient sampling and data reduction techniques for probabilistic seismic lifelines assessment. Earthquake Engineering and Structural Dynamics 39(10), 1109–1131 (2010)Google Scholar
- 7.Jensen, F., Nielsen, T.D.: Bayesian networks and decision graphs. Springer, Berlin (2007)CrossRefMATHGoogle Scholar
- 8.Kjærul, U.: Triangulation of graphs – Algorithms giving small total state space. Technical Report R 90-09, University of Aalborg, Denmark (1990)Google Scholar
- 9.Kjærul, U.: Optimal decomposition of probabilistic networks by simulated annealing. Statistics and Computing 2, 7–17 (1992)Google Scholar
- 10.Koller, D., Friedman, N.: Probabilistic Graphical Models: Principles and Techniques. MIT Press (2009)Google Scholar
- 11.Larrañaga, P., Kuijpers, C., Poza, M., Murga, R.H.: Decomposing Bayesian networks: triangulation of the moral graph with genetic algorithms. Statistics and Computing 7, 19–34 (1997)CrossRefGoogle Scholar
- 12.SYNER-G 2012, collaborative research project, funded by the European Union within Framework Programme 7 (2007-2013) under grant agreement no. 244061, http://www.syner-g.eu
- 13.Wen, W.: Optimal decomposition of belief networks. In: Proceedings of the Sixth Conference on Uncertainty in Artificial Intelligence (UAI 1990), pp. 209–224. Elsevier Science, New York (1990)Google Scholar
- 14.Tarjan, R.E., Yannakakis, M.: Simple linear-time algorithms to test chordality of graphs, test acyclicity of hypergraphs, and selectively reduce acyclic hypergraphs. SIAM Journal on Computing 13, 566–579 (1984)CrossRefMATHMathSciNetGoogle Scholar
Copyright information
© Springer International Publishing Switzerland 2014