Adaptive Gaussian process surrogates for Bayesian inference

27 Sep 2018 Timur Takhtaganov Juliane Müller

We present an adaptive approach to the construction of Gaussian process surrogates for Bayesian inference with expensive-to-evaluate forward models. Our method relies on the fully Bayesian approach to training Gaussian process models and utilizes the expected improvement idea from Bayesian global optimization... (read more)

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