Abstract. The radiological characterization of contaminated elements (walls, grounds, objects) from nuclear facilities often suffers from too few measurements. In order to determine risk prediction bounds on the level of contamination, some classic statistical methods may therefore be unsuitable, as they rely upon strong assumptions (e.g., that the underlying distribution is Gaussian) which cannot be verified. Considering that a set of measurements or their average value come from a Gaussian distribution can sometimes lead to erroneous conclusions, possibly not sufficiently conservative. This paper presents several alternative statistical approaches which are based on much weaker hypotheses than the Gaussian one, which result from general probabilistic inequalities and order-statistic based formulas. Given a data sample, these inequalities make it possible to derive prediction intervals for a random variable which can be directly interpreted as probabilistic risk bounds. For the sake of validation, they are first applied to simulated data generated from several known theoretical distributions. Then, the proposed methods are applied to two data sets obtained from real radiological contamination measurements.

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OpenTURNS is an open source library for uncertainty propagation by probabilistic methods. Developed in a partnership of five industrial companies (EDF, Airbus, Phimeca, IMACS and ONERA), it benefits from a strong practical feedback. Classical algorithms of UQ are available : central dispersion, probability of exceedance, sensitivity analysis, metamodels and stochastic processes. Developed in C++, OpenTURNS is also available as a Python module and has gained maturity thanks to more than 10 years of development.However, there are situations where the engineer in charge of performing an uncertainty study does not want to use a programming language such as C++ or Python. In this context, providing a graphical user interface (GUI) may allow to greatly increase the use of Open-TURNS and, more generally, of the UQ methodology.In this paper, we present a basic tutorial of OpenTURNS in Python and will review the new features in the library, which include new incremental statistical estimators. In the second part, we review the new features in the open source GUI will be presented.