Bayesian Analysis of the Guárico data
نویسنده
چکیده
We use a basic stationary and isotropic spatial linear regression model to fit a rainfall dataset collected in central Venezuela. First order and partial second order trend explain the mean function. With a Matérn correlation function with smoothness fixed at one, unknown parameters are estimated through MCMC method. The variogram is also estimated based on the posterior samples. By examining posterior predictive distributions at the rainfall stations, our model performs reasonably well because the observed values could be mostly replicated.
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