Author

Robert Scheichl

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Preprint Oct 2026

Reducing Complexity of Pseudo-Marginal Sequential Monte Carlo via Hierarchical Tempering

The Bayesian approach to inverse problems provides a powerful framework for inferring unknown parameters from noisy observations. Among the computationally most challenging statistical inference problems are problems with intractable likelihoods, especially if no unbiased likelihood estimator is available. We focus on...

Josef Martínek, Julian Hofstadler, Alexander M. G. Cox et al. · 0 citations

Preprint Oct 2026

Efficient localised model reduction for multiscale PDEs via Grassmannian interpolation

Multiscale, parameter-dependent partial differential equations (PDEs) pose severe computational challenges due to strong coefficient heterogeneity and high-dimensional parameter spaces. We develop a geometric interpolation approach within the multiscale generalized finite element method (MS-GFEM) that targets the most...

C. Alber, M. Bachmayr, Robert Scheichl et al. · 0 citations