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Aretha L Teckentrup
Aretha L Teckentrup
Reader in Mathematics of Data Science, University of Edinburgh
Verified email at ed.ac.uk - Homepage
Title
Cited by
Cited by
Year
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
5992011
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
2452013
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
2352013
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 3 (1), 1075-1108, 2015
161*2015
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
1392015
Posterior consistency for Gaussian process approximations of Bayesian posterior distributions
A Stuart, A Teckentrup
Mathematics of Computation 87 (310), 721-753, 2018
1122018
How deep are deep Gaussian processes?
MM Dunlop, MA Girolami, AM Stuart, AL Teckentrup
Journal of Machine Learning Research 19 (54), 1-46, 2018
1072018
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
612017
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
382020
Multilevel markov chain monte carlo
TJ Dodwell, C Ketelsen, R Scheichl, AL Teckentrup
Siam Review 61 (3), 509-545, 2019
382019
Multilevel Monte Carlo Methods and Uncertainty Quantification
AL Teckentrup
University of Bath, 2013
262013
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
232018
Multilevel Monte Carlo methods for highly heterogeneous media
AL Teckentrup
Proceedings of the 2012 Winter Simulation Conference (WSC), 1-12, 2012
142012
Optimal point sets for total degree polynomial interpolation in moderate dimensions
M Gunzburger, AL Teckentrup
arXiv preprint arXiv:1407.3291, 2014
112014
Adaptive multilevel Monte Carlo for probabilities
AL Haji-Ali, J Spence, AL Teckentrup
SIAM Journal on Numerical Analysis 60 (4), 2125-2149, 2022
62022
A locally adaptive Bayesian cubature method
M Fisher, C Oates, C Powell, A Teckentrup
International Conference on Artificial Intelligence and Statistics, 1265-1275, 2020
32020
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
32019
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
32015
Error bounds for some approximate posterior measures in Bayesian inference
HC Lie, TJ Sullivan, A Teckentrup
Numerical Mathematics and Advanced Applications ENUMATH 2019: European†…, 2020
22020
Mathematical Foundations for Data Analysis. By Jeff M. Phillips.
AL TECKENTRUP
SIAM Review Vol. 65, Issue 1 (March 2023) 65 (1), 327, 2023
2023
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Articles 1–20