Solving Bayesian inverse problems with expensive likelihoods using constrained Gaussian processes and active learning
Maximilian Dinkel,
Carolin M Geitner,
Gil Robalo Rei
et al.
Abstract:Solving inverse problems using Bayesian methods can become prohibitively expensive when likelihood evaluations involve complex and large scale numerical models. A common approach to circumvent this issue is to approximate the forward model or the likelihood function with a surrogate model. But also there, due to limited computational resources, only a few training points are available in many practically relevant cases. Thus, it can be advantageous to model the additional uncertainties of the surrogate in orde… Show more
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