Jean Baccou

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This work is devoted to the construction of new kriging-based interpolating position-dependent subdivision schemes for data reconstruction. Their originality stands in the coupling of the underlying multi-scale framework associated to subdivision schemes with kriging theory. Thanks to an efficient stencil selection, they allow to cope the problem of(More)
Multiscale transformations are being used in recent times in the first step of transform coding algorithms for image compression. A multiresolution-based compression scheme can be schematically described as follows: Assume that ¯ f L represents a sequence of data sampled at a given resolution. corresponds to a sampling of the data at a much coarser(More)
Statistical approaches such as Kriging methods ([3]) are of common use for data reconstruction. Their main advantages stand in an accurate interpolating prediction exploiting the correlation structure exhibited by the available data and the possibility to quantify the precision of a prediction thanks to the underlying probabilistic model. However, these(More)
The need to differentiate between epistemic and aleatory uncertainty is now well admitted by the risk analysis community. One way to do so is to model aleatory uncertainty by classical probability distributions and epistemic uncertainty by means of possibility distributions, and then propagate them by their respective calculus. The result of this(More)
This paper is devoted to some recent developments in uncertainty analysis methods of computer codes used for accident management procedures in nuclear industry. A quick overview on classical probabilistic methods for uncertainty methodology is first given. It turns out that despite its attractiveness relying on a simple implementation and convenient(More)
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