| Adaptive stochastic Galerkin FEM for lognormal coefficients in hierarchical tensor representations M Eigel, M Marschall, M Pfeffer, R Schneider Numerische Mathematik 145, 655-692, 2020 | 26 | 2020 |
| Sampling-free Bayesian inversion with adaptive hierarchical tensor representations M Eigel, M Marschall, R Schneider Inverse Problems 34 (3), 035010, 2018 | 25 | 2018 |
| A simple method for Bayesian uncertainty evaluation in linear models G Wübbeler, M Marschall, C Elster Metrologia 57 (6), 065010, 2020 | 17 | 2020 |
| Low-rank tensor reconstruction of concentrated densities with application to Bayesian inversion M Eigel, R Gruhlke, M Marschall Statistics and Computing 32 (2), 27, 2022 | 15 | 2022 |
| Ion type and valency differentially drive vimentin tetramers into intermediate filaments or higher order assemblies M Denz, M Marschall, H Herrmann, S Köster Soft Matter 17 (4), 870-878, 2021 | 13 | 2021 |
| Compressive nano-FTIR chemical mapping G Wübbeler, M Marschall, E Rühl, B Kästner, C Elster Measurement Science and Technology 33 (3), 035402, 2021 | 11 | 2021 |
| GUM-compliant uncertainty evaluation using virtual experiments G Wübbeler, M Marschall, K Kniel, D Heißelmann, F Härtig, C Elster Metrology 2 (1), 114-127, 2022 | 10 | 2022 |
| Rejection sampling for Bayesian uncertainty evaluation using the Monte Carlo techniques of GUM-S1 M Marschall, G Wübbeler, C Elster Metrologia 59 (1), 015004, 2021 | 9 | 2021 |
| Compressed FTIR spectroscopy using low-rank matrix reconstruction M Marschall, A Hornemann, G Wübbeler, A Hoehl, E Rühl, B Kästner, ... Optics Express 28 (26), 38762-38772, 2020 | 9 | 2020 |
| An adaptive stochastic Galerkin tensor train discretization for randomly perturbed domains M Eigel, M Marschall, M Multerer SIAM/ASA Journal on Uncertainty Quantification 8 (3), 1189-1214, 2020 | 9 | 2020 |
| Experimental design for virtual experiments in tilted-wave interferometry G Scholz, I Fortmeier, M Marschall, M Stavridis, M Schulz, C Elster Metrology 2 (1), 6, 2022 | 8 | 2022 |
| Compressed AFM-IR hyperspectral nanoimaging B Kästner, M Marschall, A Hornemann, S Metzner, P Patoka, S Cortes, ... Measurement Science and Technology 35 (1), 015403, 2023 | 3 | 2023 |
| Assessment of subsampling schemes for compressive nano-FTIR imaging S Metzner, B Kästner, M Marschall, G Wübbeler, S Wundrack, A Bakin, ... IEEE Transactions on Instrumentation and Measurement 71, 1-8, 2022 | 3 | 2022 |
| JCGM 101-compliant uncertainty evaluation using virtual experiments F Hughes, M Marschall, G Wübbeler, G Kok, M van Dijk, C Elster arXiv preprint arXiv:2404.10530, 2024 | 2 | 2024 |
| Uncertainty propagation in quantitative magnetic force microscopy using a Monte-Carlo method M Marschall, S Sievers, HW Schumacher, C Elster IEEE Transactions on Magnetics 58 (5), 1-8, 2022 | 2 | 2022 |
| Calibration method for complex permittivity measurements using s-SNOM combining multiple probe tapping harmonics D Siebenkotten, B Kästner, M Marschall, A Hoehl, S Amakawa Optics Express 32 (13), 23882-23893, 2024 | 1 | 2024 |
| Bayesian uncertainty evaluation applied to the tilted-wave interferometer M Marschall, I Fortmeier, M Stavridis, F Hughes, C Elster Optics Express 32 (11), 18664-18683, 2024 | 1 | 2024 |
| Machine learning based priors for Bayesian inversion in MR imaging M Marschall, G Wübbeler, F Schmähling, C Elster Metrologia 60 (4), 044003, 2023 | 1 | 2023 |
| On modelling of artefact instability in interlaboratory comparisons M Marschall, G Wübbeler, M Borys, C Elster Metrologia 60 (4), 045010, 2023 | 1 | 2023 |
| Generative models and Bayesian inversion using Laplace approximation M Marschall, G Wübbeler, F Schmähling, C Elster arXiv preprint arXiv:2203.07755, 2022 | 1 | 2022 |
