| Multilevel Monte Carlo methods and applications to elliptic PDEs with random coefficients KA Cliffe, MB Giles, R Scheichl, AL Teckentrup Computing and Visualization in Science 14, 3-15, 2011 | 682 | 2011 |
| Further analysis of multilevel Monte Carlo methods for elliptic PDEs with random coefficients AL Teckentrup, R Scheichl, MB Giles, E Ullmann Numerische Mathematik 125, 569-600, 2013 | 268 | 2013 |
| Finite element error analysis of elliptic PDEs with random coefficients and its application to multilevel Monte Carlo methods J Charrier, R Scheichl, AL Teckentrup SIAM Journal on Numerical Analysis 51 (1), 322-352, 2013 | 268 | 2013 |
| Multilevel Markov chain Monte Carlo TJ Dodwell, C Ketelsen, R Scheichl, AL Teckentrup Siam Review 61 (3), 509-545, 2019 | 236* | 2019 |
| A multilevel stochastic collocation method for partial differential equations with random input data AL Teckentrup, P Jantsch, CG Webster, M Gunzburger SIAM/ASA Journal on Uncertainty Quantification 3 (1), 1046-1074, 2015 | 167 | 2015 |
| How deep are deep Gaussian processes? MM Dunlop, MA Girolami, AM Stuart, AL Teckentrup Journal of Machine Learning Research 19 (54), 1-46, 2018 | 162 | 2018 |
| Posterior consistency for Gaussian process approximations of Bayesian posterior distributions A Stuart, A Teckentrup Mathematics of Computation 87 (310), 721-753, 2018 | 145 | 2018 |
| Convergence of Gaussian process regression with estimated hyper-parameters and applications in Bayesian inverse problems AL Teckentrup SIAM/ASA Journal on Uncertainty Quantification 8 (4), 1310-1337, 2020 | 86 | 2020 |
| Quasi-Monte Carlo and multilevel Monte Carlo methods for computing posterior expectations in elliptic inverse problems R Scheichl, AM Stuart, AL Teckentrup SIAM/ASA Journal on Uncertainty Quantification 5 (1), 493-518, 2017 | 74 | 2017 |
| Multilevel Monte Carlo Methods and Uncertainty Quantification AL Teckentrup University of Bath, 2013 | 32 | 2013 |
| Random forward models and log-likelihoods in Bayesian inverse problems HC Lie, TJ Sullivan, AL Teckentrup SIAM/ASA Journal on Uncertainty Quantification 6 (4), 1600-1629, 2018 | 28 | 2018 |
| Adaptive multilevel Monte Carlo for probabilities AL Haji-Ali, J Spence, AL Teckentrup SIAM Journal on Numerical Analysis 60 (4), 2125-2149, 2022 | 21 | 2022 |
| Multilevel Monte Carlo methods for highly heterogeneous media AL Teckentrup Proceedings of the 2012 Winter Simulation Conference (WSC), 1-12, 2012 | 16 | 2012 |
| Optimal point sets for total degree polynomial interpolation in moderate dimensions M Gunzburger, AL Teckentrup arXiv preprint arXiv:1407.3291, 2014 | 14 | 2014 |
| Gaussian processes for Bayesian inverse problems associated with linear partial differential equations T Bai, AL Teckentrup, KC Zygalakis Statistics and Computing 34 (4), 139, 2024 | 8 | 2024 |
| Introduction to Gaussian Process Regression in Bayesian Inverse Problems, with New Results on Experimental Design for Weighted Error Measures T Helin, AM Stuart, AL Teckentrup, KC Zygalakis International Conference on Monte Carlo and Quasi-Monte Carlo Methods in …, 2022 | 7 | 2022 |
| A locally adaptive Bayesian cubature method M Fisher, C Oates, C Powell, A Teckentrup International Conference on Artificial Intelligence and Statistics, 1265-1275, 2020 | 7 | 2020 |
| ERRATUM: A Hierarchical Multilevel Markov Chain Monte Carlo Algorithm with Applications to Uncertainty Quantification in Subsurface Flow TJ Dodwell, C Ketelsen, R Scheichl, AL Teckentrup SIAM/ASA Journal on Uncertainty Quantification 7 (4), 1398-1399, 2019 | 3 | 2019 |
| Improved multilevel Monte Carlo methods for finite volume discretisations of darcy flow in randomly layered media M Park, A Teckentrup arXiv preprint arXiv:1506.04694, 2015 | 3 | 2015 |
| Convergence rates of non-stationary and deep Gaussian process regression C Moriarty-Osborne, AL Teckentrup arXiv preprint arXiv:2312.07320, 2023 | 2 | 2023 |
Robert ScheichlHeidelberg Universityuni-heidelberg.de의 이메일 확인됨
