For water levels, generally a non-stationary variable, the technique of universal kriging is applied in preference to ordinary kriging as the interpolation method. Each set of data in every sector can fit different empirical semivariogram models since they have different spatial structures. These models can be classified as circular, spherical, tetraspherical, pentaspherical, exponential, gauss...

We investigate three new strategies for the numerical solution of optimal stopping problems within the Regression Monte Carlo (RMC) framework of Longstaff and Schwartz. First, we propose the use of stochastic kriging (Gaussian process) meta-models for fitting the continuation value. Kriging offers a flexible, nonparametric regression approach that quantifies approximation quality. Second, we co...

This paper presents a new approach for Gaussian process (GP) regression for large datasets. The approach involves partitioning the regression input domain into multiple local regions with a different local GP model fitted in each region. Unlike existing local partitioned GP approaches, we introduce a technique for patching together the local GP models nearly seamlessly to ensure that the local ...

rainfall spatial analysis methods are very helpful since there are not enough rainfall gauge stations and watersheds are scattered in large extent. there are many different methods for estimating average precipitation such as; arithmetic method and thiessen polygon. however, the arrangement and location of data and their correlations are not considered by classic methods. thus, geostatistical t...

This paper presents a method for finding the global maximum of a spatially varying field using a multi-agent system. A surrogate model of the field is determined via Kriging (Gaussian process regression) from a set of measurements collected by the agents. A criterion exploiting Kriging statistical properties is introduced for selecting new sampling points that each vehicle must rally. These new...

We tackle the problem of multi-task learning with copula process. Multivariable prediction in spatial and spatialtemporal processes such as natural resource estimation and pollution monitoring have been typically addressed using techniques based on Gaussian processes and co-Kriging. While the Gaussian prior assumption is convenient from analytical and computational perspectives, nature is domin...

Gaussian process analysis of processes with multiple outputs is limited by the fact that far fewer good classes of covariance functions exist compared with the scalar (single-output) case. To address this difficulty, we turn to covariance function models that take a form consistent in some sense with physical laws that govern the underlying simulated process. Models that incorporate such inform...