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- W3048746511 abstract "Geographic Information Systems and related technologies are routinely used to construct massive amounts of spatially oriented data. This, in turn, has generated substantial interest among statisticians for modelling and analysing large spatial datasets. Scalable spatial process models have been found especially attractive due to their richness and flexibility and, particularly so in the Bayesian paradigm, due to their presence in hierarchical model settings. A substantial amount of research articles focus upon innovative theory and more complex model development, but limited attention has been accorded to approaches for easily implementable scalable hierarchical models for the practising scientist or spatial analyst. This article outlines how point-referenced spatial process models can be cast within the framework of conjugate Bayesian linear regression that can rapidly deliver inference on spatial processes. The approach directly samples from the exact joint posterior distribution of regression parameters, the latent process and the predictive random variables, and can be easily implemented on statistical programming environments such as R." @default.
- W3048746511 created "2020-08-18" @default.
- W3048746511 creator A5069620117 @default.
- W3048746511 date "2020-01-01" @default.
- W3048746511 modified "2023-09-27" @default.
- W3048746511 title "Conjugate Bayesian Regression Models for Massive Geostatistical Data Sets" @default.
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- W3048746511 doi "https://doi.org/10.1007/978-3-030-42196-0_10" @default.
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