32 citations · 104 across the 10 of their papers we have counts for
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stat.CO2022★ 17 cited
A fully Bayesian sparse polynomial chaos expansion approach with joint priors on the coefficients and global selection of terms
Paul-Christian Bürkner, Ilja Kröker, Sergey Oladyshkin +1
Polynomial chaos expansion (PCE) is a versatile tool widely used in uncertainty quantification and machine learning, but its successful application depends strongly on the accuracy…
stat.CO2021★ 1 cited
Sequential pCN-MCMC, an efficient MCMC method for Bayesian inversion of high-dimensional multi-Gaussian priors
Sebastian Reuschen, Fabian Jobst, Wolfgang Nowak
In geostatistics, Gaussian random fields are often used to model heterogeneities of soil or subsurface parameters. To give spatial approximations of these random fields, they are d…