We introduce Latent Gaussian Process Regression which is a latent variable extension allowing modelling of non-stationary processes using stationary GP priors. The approach is built on extending the input space of a regression problem with a latent variable that is used to modulate the covariance function over the input space. We show how our approach can be used to model non-stationary process...

Kriging is a widely employed technique across computer experiments, machine learning and geostatistics. An important challenge for kriging its high costs when dealing with large datasets. This article focuses on class of methods aiming at decreasing this computational burden by aggregating predictors based smaller data subsets. More precisely, it shows that aggregation ignore the covariance bet...

The methods of classical geostatistics were primarily developed under a set of constraining assumptions (linear estimator, measurements are exacts, etc.) and they lack the theoretical underpinnings and practical flexibility to incorporate the many sources of information available in modern days sciences (such as physical laws, empirical models, higher statistical moments, uncertain information)...

We apply different advantages of the optimal genetic searching, geostatistics, and fuzzy c-means clustering to the segmentation of gray-level images. The proposed method can deal effectively with noisy image segmentation.

Geostatistics of extreme values makes it possible to model the asymptotic behavior random phenomena that depend on time or space. In this paper, we propose new models extremal coefficient a stationary field where cumulative distribution is associated with multivariate copula. More precisely, some extensions extremogram and these derivatives are built in spatial framework. Moreover, both two geo...

Geostatistical analyses were first developed in the 1950's as a result of interest in areal or block averages for ore reserves in the mining industry. Today, variogram estimation and spatial prediction (kriging) span all sciences where data exhibit spatial correlation. Theoretical properties of the spatial process are addressed under the distribution-free and likelihood-based perspectives. Stre...

The trend in interactive entertainment is towards scenes with massive numbers of characters, and requiring huge amounts of motion data, which must be compactly and efficiently stored without sacrificing quality or controllability of the motions. Multilinear algebra is a powerful tool for efficiently representing multivariate data, including human motion data, through the analysis of multimodal ...