Bayesian Geostatistical Design

By Peter J. Diggle, S. Lophaven. 2006

236 citations recorded by the source corpus.

Subfields: Environmental Engineering, Mechanical Engineering, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/f3a3aa570e6baa05863de0581a67692063c99be1

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In Environmental Engineering

In Mechanical Engineering

In Statistics and Probability

Citations in the corpus

Bayesian geostatistical design: Task‐driven optimal site investigation when the geostatistical model is uncertain (2010, methodology): “[54] The Bayesian approach to structural uncertainty honors the need for model identification, leading to a diversification of sampled lag distances [e.g., Ucinski, 2005; Diggle and Lophaven, 2006; Muller, 2007].”
A full scale approximation of covariance functions for large spatial data sets (2012, methodology): “Again, the sampling from the posterior distribution p{Y.s0/|Ω, Y} involves the inversion of the n×n matrix Cn,n + τ2I, which is a computational burden for large data sets as shown in the previous paragraph.”
Probabilistic inference of multi‐Gaussian fields from indirect hydrological data using circulant embedding and dimensionality reduction (2015, background): “Unfortunately, a lack of (sufficient) point K measurements (if any) makes it difficult to estimate directly the geostatistical parameters (mean, sill, variogram model, integral scales and anisotropy factors) from variographic analysis [Ortiz and Deutsch, 2002; Nowak et al., 2010].”
Effective Geographic Sample Size in the Presence of Spatial Autocorrelation (2005, background): “Geographic sampling networks, designed for efficient estimation of parameters describing the geographic distribution of interest, need to guard against grossly inefficient spatial prediction ( Martin 2001 ; Muller 2001 ; Diggle and Lophaven 2004 ) and vice versa.”
Inhibitory geostatistical designs for spatial prediction taking account of uncertain covariance structure (2016, methodology): “…that focuses on the goals of efficient spatial prediction in conjunction with parameter estimation includes Zhu (2002); Zhu and Stein (2006); Diggle and Lophaven (2006); Pilz and Spöck (2006); Zimmerman (2006); Banerjee, Gelfand, Finley, and Sang (2008); Bijleveld et al. (2012); Müller et…”
Gaussian predictive process models for large spatial data sets (2008, methodology): “In addition to regular lattices or grids, we also explored two different knot configurations which were described by Diggle and Lophaven (2006) in spatial design contexts.”
A Review of Modern Computational Algorithms for Bayesian Optimal Design (2016, background): “For example, Diggle and Lophaven (2006) propose a Bayesian design criterion that chooses a set of sampling locations to enable efficient spatial prediction by minimising the expectation of the spatially averaged prediction variance (with respect to the marginal distribution of the data).”
Spatial sampling design and covariance-robust minimax prediction based on convex design ideas (2010, background): “The importance of (optimal) spatial sampling design considerations for environmental applications has been demonstrated in quite a few papers and monographs, we mention Brus and de Gruijter (1997), Diggle and Lophaven (2006) and Brus and Heuvelink (2007).”