Bayesian calibration of a large‐scale geothermal reservoir model by a new adaptive delayed acceptance Metropolis Hastings algorithm

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Efficient posterior exploration of a high‐dimensional groundwater model from two‐stage Markov chain Monte Carlo simulation and polynomial chaos expansion (2013, methodology): “Strategy B [42] In our second sampling scheme, herein referred to as strategy B, we adopt a state-of-the-art two-stage MCMC approach [Cui et al., 2011], in which ~F P is constructed beforehand and serves as reduced-order model in the first stage.”
Survey of multifidelity methods in uncertainty propagation, inference, and optimization (2018, methodology): “Multifidelity methods in MCMC typically use multistage adaptive delayed acceptance formulations that leverage low-fidelity models to speed up the sampling [44, 62, 51, 54].”
Review of surrogate modeling in water resources (2012, methodology): “Cui et al. [2011] also develop a ‘‘reduced order model’’ of a ‘‘fine model’’ by coarsening the gird structure and employ it within a correction function framework to enhance the efficiency of an MCMC algorithm for groundwater model inversion; an adaptive local correction function is used in their st”