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Uncertainty is an inherent characteristic of biological and geospatial datawhich is almost made by measurement error in the observed values of thequantity of interest. Ignoring measurement error can lead to biased estimatesand inflated variances and so an inappropriate inference. In this paper, theGaussian spatial model is fitted based on covariate measurement error. For thispurpose, we adopt the Bayesian approach and utilize the Markov chain MonteCarlo algorithms and data augmentations to carry out calculations. Themethodology is illustrated using simulated data.

Analysis of Gaussian Spatial Models with Covariate Measurement Error