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The paper discusses a Bayesian approach to geostatistical design, focusing on selecting optimal sampling locations for spatial prediction while accounting for parameter uncertainty. It distinguishes between retrospective design, which modifies existing sampling locations, and prospective design, which establishes new locations. The authors propose a Bayesian design criterion that emphasizes efficient spatial prediction, contrasting it with traditional methods that may not optimize for this goal.

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st125745

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EN