This paper compares several Gaussian-processbased surrogate modeling methods applied to black-box optimization by means of the Covariance Matrix Adaptation Evolution Strategy (CMA-ES), which is considered state-of-the-art in the area of continuous black-box optimization. Among the compared methods are the Modelassisted CMA-ES, the Robust Kriging Metamodel CMAES, and the Surrogate CMA-ES. In add...

The joint log-likelihood of a normally-distributed vector can be expressed as a linear combination of the log-likelihoods of each marginal variable in that random vector. The coefficients of this linear combination are linked to Fisher information concepts, but also to the kriging weights, when the random vector is a spatial distribution. This allows the exact estimation of the posterior or the...

A practical problem in spatial statistics is that of constructing spatial sampling designs for environmental monitoring networks. Among the several purposes for which a monitoring network may be designed for, there is that of interpolation. In this paper, a criterion for spatial designs that emphasize the utility of the network for spatial interpolation of a random field X is discussed. Within ...

Abstract: We consider the use of randomised forward models and log-likelihoods within the Bayesian approach to inverse problems. Such random approximations to the exact forward model or log-likelihood arise naturally when a computationally expensive model is approximated using a cheaper stochastic surrogate, as in Gaussian process emulation (kriging), or in the field of probabilistic numerical ...

This paper presents the application of Exploratory Spatial Data Analysis (ESDA) and Kriging from GIS (ArcGIS8.3) in disease mapping through the analysis of hepatitis B in China. The research shows that geostatistical analysis techniques such as Kriging and ESDA have a good effect in disease mapping. Kriging methods can express properly the spatial correlation. Furthermore, unlike model-based me...

Spatial estimations are increasingly used to estimate geocoded ambient particulate matter (PM) concentrations in epidemiologic studies because measures of daily PM concentrations are unavailable in most U.S. locations. This study was conducted to a) assess the feasibility of large-scale kriging estimations of daily residential-level ambient PM concentrations, b) perform and compare cross-valida...

This paper applies a novel Kriging model to the interpolation of stochastic simulation with high computational expense. The novel Kriging model is developed by using Taylor expansion to construct a drift function for Kriging, thus named Taylor Kriging. The interpolation capability of Taylor Kriging for stochastic simulation is empirically compared with those of Simple Kriging and Ordinary Krigi...

In geostatistics, spatial data will be analysed that often come from irregularly distributed sampling locations. Interest is in modelling the data, i.e. estimating distributional parameters, and then to predict the phenomenon under study at unobserved sites within the corresponding sampling domain. The method of universal kriging for spatial prediction was introduced to cover the problem of spa...

In geostatistics, spatial data will be analysed that often come from irregularly distributed sampling locations. Interest is in modelling the data, i.e. estimating distributional parameters, and then to predict the phenomenon under study at unobserved sites within the corresponding sampling domain. The method of universal kriging for spatial prediction was introduced to cover the problem of spa...

This paper presents the applications of Kriging spatial interpolation methods for meteorologic variables, including temperature and relative humidity, in regions of Vietnam. Three types of interpolation methods are used, which are as follows: Ordinary Kriging, Universal Kriging, and Universal Kriging plus Digital Elevation model correction. The input meteorologic data was collected from 98 grou...