79 citations · 258 across the 61 of their papers we have counts for
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Tractable Optimal Experimental Design using Transport Maps
Karina Koval, Roland Herzog, Robert Scheichl
We present a flexible method for computing Bayesian optimal experimental designs (BOEDs) for inverse problems with intractable posteriors. The approach is applicable to a wide rang…
Multilevel Delayed Acceptance MCMC with an Adaptive Error Model in PyMC3
Mikkel B. Lykkegaard, Grigorios Mingas, Robert Scheichl +2
Uncertainty Quantification through Markov Chain Monte Carlo (MCMC) can be prohibitively expensive for target probability densities with expensive likelihood functions, for instance…
Multilevel Dimension-Independent Likelihood-Informed MCMC for Large-Scale Inverse Problems
Tiangang Cui, Gianluca Detommaso, Robert Scheichl
We present a non-trivial integration of dimension-independent likelihood-informed (DILI) MCMC (Cui, Law, Marzouk, 2016) and the multilevel MCMC (Dodwell et al., 2015) to explore th…