Incorporating Posterior Model Discrepancy into a Hierarchical Framework to Facilitate Out-of-the-Box MCMC Sampling for Geothermal Inverse Problems and Uncertainty Quantification.

@inproceedings{Maclaren2018IncorporatingPM,
  title={Incorporating Posterior Model Discrepancy into a Hierarchical Framework to Facilitate Out-of-the-Box MCMC Sampling for Geothermal Inverse Problems and Uncertainty Quantification.},
  author={Oliver J. Maclaren and Ruanui Nicholson and Elvar K. Bjarkason and Michael J. O'Sullivan},
  year={2018}
}
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… CONTINUE READING
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