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Incorporating Posterior Model Discrepancy into a Hierarchical Framework to Facilitate Out-of-the-Box MCMC Sampling for Geothermal Inverse Problems and Uncertainty Quantification. (arXiv:1810.04350v1 [
来源于:arXiv
We consider geothermal inverse problems and uncertainty quantification from a
Bayesian perspective. Our goal is to make standard, 'out-of-the-box' Markov
chain Monte Carlo (MCMC) sampling more feasible for complex simulation models.
To do this, we first show how to pose the inverse and prediction problems in a
hierarchical Bayesian framework. We then show how to incorporate so-called
posterior model approximation error into this hierarchical framework, using a
modified form of the Bayesian approximation error (BAE) approach. This enables
the use of a 'coarse', approximate model in place of a finer, more expensive
model, while also accounting for the additional uncertainty and potential bias
that this can introduce. Our method requires only simple probability modelling
and only modifies the target posterior - the same standard MCMC sampling
algorithm can be used to sample the new target posterior. We show that our
approach can achieve significant computational speed-ups on a geothermal 查看全文>>