Model selection for Gaussian processes utilizing sensitivity of posterior predictive distribution
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We propose two novel methods for simplifying Gaussian process (GP) models by examining the predictions of a full model in the vicinity of the training points and thereby ordering the covariates based on their predictive relevance. Our results on synthetic and real world data sets demonstrate improved variable selection compared to automatic relevance determination (ARD) in terms of consistency and predictive performance. We expect our proposed methods to be useful in interpreting and understanding complex Gaussian process models.
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