| Analysis of the ensemble and polynomial chaos Kalman filters in Bayesian inverse problems OG Ernst, B Sprungk, HJ Starkloff SIAM/ASA Journal on Uncertainty Quantification 3 (1), 823-851, 2015 | 114 | 2015 |
| On a generalization of the preconditioned Crank–Nicolson Metropolis algorithm D Rudolf, B Sprungk Foundations of Computational Mathematics 18, 309-343, 2018 | 90 | 2018 |
| On the convergence of the Laplace approximation and noise-level-robustness of Laplace-based Monte Carlo methods for Bayesian inverse problems C Schillings, B Sprungk, P Wacker Numerische Mathematik 145, 915-971, 2020 | 86 | 2020 |
| Convergence of sparse collocation for functions of countably many Gaussian random variables (with application to elliptic PDEs) OG Ernst, B Sprungk, L Tamellini SIAM Journal on Numerical Analysis 56 (2), 877-905, 2018 | 59 | 2018 |
| On the local Lipschitz stability of Bayesian inverse problems B Sprungk Inverse Problems 36 (5), 055015, 2020 | 51 | 2020 |
| Bayesian inverse problems and Kalman filters OG Ernst, B Sprungk, HJ Starkloff Extraction of Quantifiable Information from Complex Systems, 133-159, 2014 | 38 | 2014 |
| On the convergence of adaptive stochastic collocation for elliptic partial differential equations with affine diffusion M Eigel, OG Ernst, B Sprungk, L Tamellini SIAM Journal on Numerical Analysis 60 (2), 659-687, 2022 | 24 | 2022 |
| Stochastic collocation for elliptic PDEs with random data: the lognormal case OG Ernst, B Sprungk Sparse Grids and Applications-Munich 2012, 29-53, 2014 | 21 | 2014 |
| The linear conditional expectation in Hilbert space I Klebanov, B Sprungk, TJ Sullivan Bernoulli 27 (4), 2267-2299, 2021 | 17 | 2021 |
| On a Metropolis–Hastings importance sampling estimator D Rudolf, B Sprungk | 17 | 2020 |
| On expansions and nodes for sparse grid collocation of lognormal elliptic PDEs OG Ernst, B Sprungk, L Tamellini Sparse Grids and Applications-Munich 2018, 1-31, 2021 | 16 | 2021 |
| Quantitative spectral gap estimate and Wasserstein contraction of simple slice sampling V Natarovskii, D Rudolf, B Sprungk | 15 | 2021 |
| Geometric convergence of elliptical slice sampling V Natarovskii, D Rudolf, B Sprungk International Conference on Machine Learning, 7969-7978, 2021 | 12 | 2021 |
| Wasserstein sensitivity of risk and uncertainty propagation OG Ernst, A Pichler, B Sprungk SIAM/ASA Journal on Uncertainty Quantification 10 (3), 915-948, 2022 | 11 | 2022 |
| The information content of credit ratings: evidence from European convertible bond markets S Hundt, B Sprungk, A Horsch The European Journal of Finance 23 (14), 1414-1445, 2017 | 11 | 2017 |
| Numerical methods for Bayesian inference in Hilbert spaces DMB Sprungk | 10 | 2017 |
| Weak Poincar\'e inequality comparisons for ideal and hybrid slice sampling S Power, D Rudolf, B Sprungk, AQ Wang arXiv preprint arXiv:2402.13678, 2024 | 8 | 2024 |
| Dimension‐independent Markov chain Monte Carlo on the sphere HC Lie, D Rudolf, B Sprungk, TJ Sullivan Scandinavian Journal of Statistics 50 (4), 1818-1858, 2023 | 7 | 2023 |
| Robust random walk-like Metropolis-Hastings algorithms for concentrating posteriors D Rudolf, B Sprungk arXiv preprint arXiv:2202.12127, 2022 | 7 | 2022 |
| Wasserstein convergence rates of increasingly concentrating probability measures M Hasenpflug, D Rudolf, B Sprungk The Annals of Applied Probability 34 (3), 3320-3347, 2024 | 5 | 2024 |
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