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International audience; This paper addresses stochastic geometry-driven im- age models and its application to super-resolution issues. Whereas most stochastic image models rely on some priors on the distribution of grey-level configurations (e.g., patch- based models, Markov priors, multiplicative cascades,...), we here focus on geometric priors. We aim at simulating tex- ture samples while controlling high-resolution geometrical features. In this respect, we introduce a stochastic model for texture orientation fields stated as a 2D Orstein-Uhlenbeck process. We show that this process resorts in the stationary case to priors on orientation statistics. We exploit this model to state image super-resolution as a geometry-driven vari- ational minimization, where the geometry is sampled from the proposed conditional 2D Orstein-Uhlenbeck process. We demonstrate the relevance of this approach for real images as- sociated with the remote sensing of ocean surface dynamics.

SummaryWe consider the sample size determination (SSD) problem, which is a basic yet extremely important aspect of experimental design. Specifically, we deal with the Bayesian approach to SSD, which gives researchers the possibility of taking into account pre-experimental information and uncertainty on unknown parameters. At the design stage, this fact offers the advantage of removing or mitigating typical drawbacks of classical methods, which might lead to serious miscalculation of the sample size. In this context, the leading idea is to choose the minimal sample size that guarantees a probabilistic control on the performance of quantities that are derived from the posterior distribution and used for inference on parameters of interest. We are concerned with the use of historical data—i.e. observations from previous similar studies—for SSD. We illustrate how the class of power priors can be fruitfully employed to deal with lack of homogeneity between historical data and observations of the upcoming experiment. This problem, in fact, determines the necessity of discounting prior information and of evaluating the effect of heterogeneity on the optimal sample size. Some of the most popular Bayesian SSD methods are reviewed and their use, in concert with power priors, is illustrated in several medical experimental contexts.

For lots of traffic engineering tasks, telecommunications operators need good knowledge about the traffic which transit through their networks. This information is fully represented by the matrix of the volumes of data which go from any entry node to any exit node during a period of time. This matrix is called the origin-destination (OD) traffic matrix. However such a matrix is not directly available. Only measures of the volumes of data which transit through a link between routers can be obtained easily with the help of Simple Network Management Protocol (SNMP). These measures are called link counts.

Summary - Mixed linear models for maternal effects include fixed and random elements, and dispersion parameters (variances and covariances). In this paper a Bayesian model for inferences about such parameters is presented. The model includes a normal likelihood for the data, a "flat" prior for the fixed effects and a multivariate normal prior for the direct and maternal breeding values. The prior distribution for the genetic variancecovariance components is in the inverted Wishart form and the environmental components follow inverted prior distributions. The kernel of the joint posterior density of the dispersion parameters is derived in closed form. Additional numerical and analytical methods of interest that are suggested to complete a Bayesian analysis include MonteCarlo Integration, maximum entropy fit, asymptotic approximations, and the TierneyKadane approach to marginalization. maternal effect / Bayesian method / dispersion parameter