Geostatistical methods for modelling non-stationary patterns in disease risk

Projected non-stationary simultaneous iterative methods

In this paper, we study Projected non-stationary Simultaneous It-erative Reconstruction Techniques (P-SIRT). Based on algorithmic op-erators, convergence result are adjusted with Opial’s Theorem. The advantages of P-SIRT are demonstrated on examples taken from to-mographic imaging.

Scaling Multiple Point Statistics for Non-Stationary Geostatistical Modeling

Multiple-point statistics are used in geostatistical simulation to improve forecasting of responses that are highly dependent on the reproduction of complex features of the phenomenon that cannot be captured by conventional two-point simulation methods. Inference of multiple-point statistics is often based on a training image that depicts the features that provide the character to the geologica...

Bayesian modelling of geostatistical malaria risk data.

Bayesian geostatistical models applied to malaria risk data quantify the environment-disease relations, identify significant environmental predictors of malaria transmission and provide model-based predictions of malaria risk together with their precision. These models are often based on the stationarity assumption which implies that spatial correlation is a function of distance between locatio...

Non - Stationary Bayesian Modelling and

Assessment of Geostatistical Methods for Determining Distribution Patterns of Groundwater Resources in Sari-Neka Coastal Plain, Northern Iran