Gaussian models are frequently used within spatial statistics and often as a latent Gaussian model is hierachical formulations. The devellopment of Markov chain Monte Carlo methods also allow for spatial analysis of non-Gaussian observations like spatial count and survial data. Although MCMC is doable it is not without practical hassle like long computing time and slow convergence.In this talk, I will present an alternative strategy, for which the aim is to approximate all posterior marginals for the hyperparameters and the latent field. The new approach is deterministic and make use of nested integrated nested Laplace approximations. The result is, that we can directly compute very accurate approximations to the posterior marginals. It is our experience that these are in practice exact, meaning that a well-designed MCMC algorithm has to run much longer than usual to detect any error. The main benefit is the dramatic cut in computational costs: where MCMC algorithms need hours and days to run, our approximations provide more precise estimates in seconds and minutes. This talk is based on joint work with Sara Martino (NTNU), Nicolas Chopin (ENSAE) and Jo Eidsvik (NTNU). Havard Rue - Norwegian University of Science and Technology Bande son disponible au format mp3 Durée : 44 mn