Parametrization of Probabilistic Risk Models

Probabilistic risk models are popular due to their ability to capture uncertainty. However, the parametrization of such models may be challenging, especially in the context of critical infrastructures where data is sometimes sparse. In this paper we propose different methods to parametrize a stochastic model of risk propagation depending on the amount of information available. Two of the approaches are illustrated with an example of a critical infrastructure and the application of the other methods is sketched.