A Bayesian Prediction using the Elliptical and the Skew Gaussian Processes
نویسندگان
چکیده
A Bayesian Prediction using the Elliptical Processes (EP) and the Skew Gaussian Processes (SGP) is proposed, motivated by a Bayesian model for heavy, light tailed or skewed real data. We define weak third order stationary for the Skew Gaussian Processes. Sometimes the family of distributions have dimensional coherency (consistency) property which is important for prediction. We use a Markov Chain Monte Carlo (MCMC) method to generate samples from a posterior distribution and use the accept-reject algorithm with a latent variable within the Gibbs Sampler to generate values from a predictive distribution. Finally a Skew Gaussian Process is applied to rainfall data near Darwin, Australia.
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