On Prediction Properties of Kriging: Uniform Error Bounds and Robustness

Kriging based on Gaussian random fields is widely used in reconstructing unknown functions. The kriging method has pointwise predictive distributions which are computationally simple. However, in many applications one would like to predict for a range of untried points simultaneously. In this work we obtain some error bounds for the (simple) kriging predictor under the uniform metric. It works for a scattered set of input points in an arbitrary dimension, and also covers the case where the covariance function of the Gaussian process is misspecified. These results lead to a better understanding of the rate of convergence of kriging under the Gaussian or the Mat\'ern correlation functions, the relationship between space-filling designs and kriging models, and the robustness of the Mat\'ern correlation functions

Optimization of scale-free network for random failures

It has been found that the networks with scale-free distribution are very resilient to random failures. The purpose of this work is to determine the network design guideline which maximize the network robustness to random failures with the average number of links per node of the network is constant. The optimal value of the distribution exponent and the minimum connectivity to different network size are given in this paper. Finally, the optimization strategy how to improve the evolving network robustness is given.Comment: 6 pages, 1 figur