An interleaved adaptive Gaussian process surrogate that optimizes both evaluation locations and tolerances can reduce the computational cost of posterior sampling in Bayesian inverse problems.
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Posterior sampling with Adaptive Gaussian Processes in Bayesian parameter identification
An interleaved adaptive Gaussian process surrogate that optimizes both evaluation locations and tolerances can reduce the computational cost of posterior sampling in Bayesian inverse problems.