Mathieu Couplet

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We consider a model for the risk-based design of a flood protection dike, and use probability distributions to represent aleatory uncertainty and possibility distributions to describe the epistemic uncertainty associated to the poorly known parameters of such probability distributions. A hybrid method is introduced to hierarchically propagate the two types(More)
The inverse problem considered here is the estimation of the distribution of a nonobserved random variable X , linked through a time-consuming physical model H to some noisy observed data Y . Bayesian inference is considered to account for prior expert knowledge on X in a small sample size setting. A Metropolis-Hastings-within-Gibbs algorithm is used to(More)
Nicola Pedroni, E Zio, A Pasanisi, M Couplet. Empirical comparison of two methods for the Bayesian update of the parameters of probability distributions in a two-level hybrid probabilistic-possibilistic uncertainty framework for risk assessment. ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering, American Society of(More)
Models for the assessment of the risk of complex engineering systems are affected by uncertainties due to the randomness of several phenomena involved and the incomplete knowledge about some of the characteristics of the system. The objective of this article is to provide operative guidelines to handle some conceptual and technical issues related to the(More)
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