Bayesian Inference in Gaussian Model-based Geostatistics

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Bayesian geostatistical design: Task‐driven optimal site investigation when the geostatistical model is uncertain (2010, methodology): “Note that the entire distribution p(s, b, q) has been jointly conditioned on yo [see Kitanidis, 1986; Pardo‐Iguzquiza, 1999; Woodbury and Ulrych, 2000; Diggle and Ribeiro, 2002 ].”
Spatial sampling design for prediction with estimated parameters (2006, background): “Zimmerman and Cressie (1992) proposed to use a different estimator for estimating the prediction error when the spatial correlation is known to be weak, and Diggle and Ribeiro (2002) among others promoted the use of Bayesian inference.”
A Bayesian approach to stochastic capture zone delineation incorporating tracer arrival times, conductivity measurements, and hydraulic head observations (2003, methodology): “This distribution does not necessarily correspond to a standard probability distribution, and inference by numerical simulation using a Monte Carlo sampling algorithm is adopted [Diggle and Ribeiro, 2002; Feyen et al., 2002].”
Estimating variograms of soil properties by the method‐of‐moments and maximum likelihood (2000, methodology): “A further argument against the method-of-moments has recently been stated by Diggle & Ribeiro (1999). Although Ã (k) de®ned in Equation (3) is an unbiased estimate of the variogram, they suggest that, because a set of estimates derived from the same data for a sequence of lag classes will be strongl”