The complexity of the cities’ layout and other public spaces, together with the large number of people involved leads to increased strain on the resources of emergency responders. An accident, such as a fire, remains a rare event so it is difficult for those in charge of preparing for an emergency and deciding on the acceptability of risk to get a picture of such an event. The interest of all emergency response agencies is to minimize the impact of disaster events on the entities of interest, which include first of all the human population. For this, there is need for a tool that helps the decision makers estimate the distribution of the fire outcome, given different information about the environment in which the fire takes place. This paper discusses the possibility of using continuous Bayesian belief nets for the study of the factors that influence the risk to which the people involved in a building fire are exposed, and how these factors influence the risk. The big advantage of Bayesian belief net approach is that it can model uncertain events. The distribution of the variables of interest can be easily updated given information about some of the other variables. Moreover, the intuitive visual representation of the problem at hand can help people to understand complex systems or processes, like a fire in a building. In this study, the approach is tested for a small example and the results are analyzed. The possibility of extending this method to a more complex model is discussed.

1.
Ramachandran, G., 1975, “Extreme order statistics in large sample from exponential type distribution and their applications to fire loss,” In: Statistical Distribution in Scientific Work, G.P. Patil et al., eds., Vol. 2, pp. 355–367.
2.
Ramachandran, G., 1978, “Extreme value theory and large fires loss,” The ASTIN Bull., Vol. VII.
3.
U.S. Fire Administration/ National Fire Data Center, 2004, “Fire in the United States 1992–2001,” Thirteenth Edition (http://www.usfa.fema.gov/applications/publications/).
4.
Olenick
S.
and
Carpenter
D. J.
,
2003
, “
An updated international survey of computer models for fire and smoke
,”
Journal of Fire protection Engineering
,
13
(
2
), pp.
87
110
.
5.
www.firemodelsurvey.com
6.
Fenton
N.
and
Neil
M.
,
2001
, “
Making decisions: using Bayesian nets and MCDA
,”
Knowledge-Based Systems
14
, pp.
307
325
.
7.
Jensen, F.V., 1996, An Introduction to Bayesian Networks, Springer-Verlag, New York.
8.
Jensen, F.V., 2001, Bayesian Networks and Decision Graphs, Springer-Verlag, New York.
9.
http://www.ai.mit.edu/~murphyk/Bayes/bnsoft.html
10.
Lucas
P.
,
2001
, “
Bayesian model-based diagnosis
,”
International Journal of Approximate Reasoning
,
27
(
2
), pp.
99
119
.
11.
Barco, R., Guerreri, R., Hylander, G., Nielsen, L., Partanen, M., Patel, S., 2002, “Automated Troubleshooting of Mobile Networks using Bayesian Network,” Proc. IASTED International Conference on Communications Systems and Networks, ACTA Press, pp. 105–110.
12.
Littlewood, B., Strigini, L., Wright, D., Fenton, N., Neil, M., 2000, “Bayesian Belief Networks for Safety Assessment of Computer-based Systems,” In: System Performance Evaluation Methodology and Applications, Gelenbe, E., eds., CRC Press, Boca Raton, pp. 349–364.
13.
Roelen, A.L.C., Wever, R., Cooke, R.M., Lopuhaa, R., Hale, A.R., Goossens, L.H.J., 2003, “Aviation Causal Model using Bayesian Belief Nets to quantify Management Influence,” In: Safety and Reliability, proceedings of ESREL 2003, European Safety and Reliability Conference, Bedford and van Gelder, eds., pp. 1315–1320.
14.
Kurowicka, D., and Cooke, R.M., 2002, “The vine copula method for representing high dimensional dependent distributions; application to continuous belief nets,” In: Proc of the 2002. Winter Simulation Conference, Yucesan, E. et al., eds., pp. 270–278.
15.
Shachter
R. D.
, and
Kenley
C. R.
,
1989
, “
Gaussian Influence Diagrams
,”
Management Science
,
35
, pp.
527
550
.
16.
Sime, J.D., 1990, The Concept of ’Panic’, Fires and Human Behaviour, Second Edition, Canter, D., eds.
17.
Cooke, R.M., 1991, Experts in Uncertainty, New York, Oxford University Press.
18.
Yule, G.U., Kendall, M.G., 1965, An introduction to the theory of statistics, Charles Griffin & Co., 14th edition, London
19.
Kurowicka, D. and Cooke, R.M., 2004, “Non-Parametric continuous Bayesian belief nets with expert judgment,” Proc. Probabilistic Safety Assessment and Management, p. 2785–2790.
20.
Kraan, B.C.P., 2002, “Probabilistic Inversion in Uncertainty Analysis and related topics,” PhD thesis, TU Delft.
21.
http://www.norsve.com
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