Abstract:Spatially dependent data arises in many biometric applications, and Gaussian processes are a popular modelling choice for these scenarios. While Bayesian analyses of these problems have proven to be successful, selecting prior distributions for these complex models remains a difficult task. In this work, we propose a principled approach for setting prior distributions for spatial covariance parameters by placing a prior distribution on a measure of model fit.In particular, we derive the distribution of the pri… Show more
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