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Bayesian Geostatistical Design
Author(s) -
DIGGLE PETER,
LOPHAVEN SØREN
Publication year - 2006
Publication title -
scandinavian journal of statistics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.359
H-Index - 65
eISSN - 1467-9469
pISSN - 0303-6898
DOI - 10.1111/j.1467-9469.2005.00469.x
Subject(s) - sampling design , geostatistics , sampling (signal processing) , bayesian probability , mathematics , set (abstract data type) , range (aeronautics) , statistics , data mining , kriging , point (geometry) , computer science , spatial variability , engineering , population , demography , geometry , filter (signal processing) , sociology , computer vision , programming language , aerospace engineering
. This paper describes the use of model‐based geostatistics for choosing the set of sampling locations, collectively called the design, to be used in a geostatistical analysis. Two types of design situation are considered. These are retrospective design, which concerns the addition of sampling locations to, or deletion of locations from, an existing design, and prospective design, which consists of choosing positions for a new set of sampling locations. We propose a Bayesian design criterion which focuses on the goal of efficient spatial prediction whilst allowing for the fact that model parameter values are unknown. The results show that in this situation a wide range of inter‐point distances should be included in the design, and the widely used regular design is often not the best choice.