Le Grand Retour ou les Grands Adieux ? Le roman de l’exil chez Kundera

Milan Kundera, an intellectual born in Moravia who emigrated to France in 1975, in L’Ignorance leans on the myth of Ulysses to question contemporary realities of exile and return, nostalgia and oblivion. Does the hope of returning to the place of origin really haunt the modern émigré? To what extent does the notion of homeland still have meaning for him? And what happens when the émigré, unlike Ulysses the great nostalgic, prefers to stay with Calypso his lover rather than return to his native land and faithful wife Penelope? With some cynicism, Kundera in L’Ignorance offers scenarios of exile which desecrate and destabilize historically and culturally available standards while allowing us to reflect on new paradigmatic figures of contemporary exile.

Fast Update of Conditional Simulation Ensembles

Gaussian random field (GRF) conditional simulation is a key ingredient in many spatial statistics problems for computing Monte-Carlo estimators and quantifying uncertainties on non-linear functionals of GRFs conditional on data. Conditional simulations are known to often be computer intensive, especially when appealing to matrix decomposition approaches with a large number of simulation points. This work studies settings where conditioning observations are assimilated batch sequentially, with one point or a batch of points at each stage. Assuming that conditional simulations have been performed at a previous stage, the goal is to take advantage of already available sample paths and by-products to produce updated conditional simulations at mini- mal cost. Explicit formulae are provided, which allow updating an ensemble of sample paths conditioned on n ≥ 0 observations to an ensemble conditioned on n + q observations, for arbitrary q ≥ 1. Compared to direct approaches, the proposed formulae proveto substantially reduce computational complexity. Moreover, these formulae explicitly exhibit how the q new observations are updating the old sample paths. Detailed complexity calculations highlighting the benefits of this approach with respect to state-of-the-art algorithms are provided and are complemented by numerical experiments.