Results 71 to 80 of about 1,579 (110)
Assessing the impact of hard data patterns on Bayesian Maximum Entropy: a simulation study. [PDF]
Sci RepGongnet EE +4 more
europepmc +1 more source
Protocol for spatial prediction of soil transmitted helminth prevalence in the Western Pacific region using a meta-analytical approach. [PDF]
Syst RevGilmour B +14 more
europepmc +1 more source
Using ESPEN data for evidence-based control of neglected tropical diseases in sub-Saharan Africa: A comprehensive model-based geostatistical analysis of soil-transmitted helminths. [PDF]
PLoS Negl Trop DisKhaki JJ, Minnery M, Giorgi E.
europepmc +1 more source
Investigating the spatial variability of soil parameters and mineralogical characterization in the tea growing area of Kishanganj district Bihar India. [PDF]
Sci RepKumari M +9 more
europepmc +1 more source
Some of the next articles are maybe not open access.
2006
Abstract Extensive geostatistical modeling of data from the Bachaquero fields (east coast of Lake Maracaibo, Venezuela) has been conducted within the framework of a large-scale integrated study. These stochastic models have been used for several reservoir-management applications, where a fast and effective response is necessary to ...
L. Cosentino +2 more
openaire +2 more sources
Abstract Extensive geostatistical modeling of data from the Bachaquero fields (east coast of Lake Maracaibo, Venezuela) has been conducted within the framework of a large-scale integrated study. These stochastic models have been used for several reservoir-management applications, where a fast and effective response is necessary to ...
L. Cosentino +2 more
openaire +2 more sources
The American Statistician, 1989
Most data have a space and time label associated with them; data that are close together are usually more correlated than those that are far apart. Prediction (or forecasting) of a process at a particular label where there is no datum, from observed nearby data, is the subject of this article.
openaire +3 more sources
Most data have a space and time label associated with them; data that are close together are usually more correlated than those that are far apart. Prediction (or forecasting) of a process at a particular label where there is no datum, from observed nearby data, is the subject of this article.
openaire +3 more sources