Maximum Error Estimation of Gaussian Processes in the Sampling-Reconstruction Procedure

Abstract

The Sampling-Reconstruction Procedure (SRP) of Gaussian processes is investigated in this paper on the basis of the conditional mean rule. The main advantage of this methodology is that it can estimate the reconstruction error on the whole time domain, so we have the possibility to evaluate this error in any point of interest of the analyzed process. The most important points are them, where maximum levels of error are produced. Considering the above, our essential necessity is to estimate these maxima and get an easier formula in order to make a faster error evaluation with a specific sampling interval for a singular application. Initially, the analysis is performed for two Gaussian processes: one with Markovian characteristics and other with non-Markovian properties.