Most of my publications are available as preprints on arXiv. See also my page on Google Scholar.
- C. J. Oates, T. Karvonen, A. L. Teckentrup, M. Strocchi, S. A. Niederer. Probabilistic Richardson Extrapolation, Available as arXiv preprint arXiv:2401.07562.
- C. Moriarty-Osborne, A.L. Teckentrup. Convergence rates of non-stationary and deep Gaussian process regression, Available as arXiv preprint arXiv:2312.07320.
- T. Bai, A.L. Teckentrup, K.C. Zygalakis. Gaussian processes for Bayesian inverse problems associated with linear partial differential equations, Available as arXiv preprint arXiv:2307.08343.
- A. Istratuca, A.L. Teckentrup. Smoothed Circulant Embedding with Applications to Multilevel Monte Carlo Methods for PDEs with Random Coefficients. Available as arXiv preprint arXiv:2306.13493.
- M. Brolly, J.R. Maddison, J. Vanneste, A.L. Teckentrup. Bayesian comparison of stochastic models of dispersion. Journal of Fluid Mechanics, 944, A2, 2022. Available as arXiv preprint arXiv:2201.01581.
- A.-L. Haji-Ali, J. Spence, A.L. Teckentrup. Adaptive Multilevel Monte Carlo for Probabilities. SIAM Journal on Numerical Analysis, 60(4), 2125-2149, 2022. Available as arXiv preprint arXiv:2107.09148.
- A.L. Teckentrup. Convergence of Gaussian process regression with estimated hyper-parameters and applications in Bayesian inverse problems. SIAM/ASA Journal on Uncertainty Quantification, 8(4), p. 1310-1337, 2020. Available as arXiv preprint arXiv:1909.00232.
-T.J. Dodwell, C. Ketelsen, R. Scheichl, A.L. Teckentrup. Multilevel Markov Chain Monte Carlo. SIAM Review, 61(3), 509-545, 2019. [SIGEST paper]
- H.C. Lie, T.J. Sullivan, A.L. Teckentrup. Random forward models and log-likelihoods in Bayesian inverse problems. SIAM/ASA Journal on Uncertainty Quantification, 6(4), 1600-1629, 2018. Available as arXiv preprint arXiv:1712.05717.
- M.M. Dunlop, M. Girolami, A.M. Stuart, A.L. Teckentrup. How deep are deep Gaussian processes? Journal of Machine Learning Research, 19, 1-46, 2018. Available as arXiv preprint arXiv:1711.11280.
- A.M. Stuart, A.L. Teckentrup. Posterior Consistency for Gaussian Process Approximations of Bayesian Posterior Distributions. Mathematics of Computation, (87), 721-753, 2018. Available as arXiv preprint arXiv:1603.02004.
- R. Scheichl, A.M. Stuart, A.L. Teckentrup. Quasi-Monte Carlo and Multilevel Monte Carlo Methods for Computing Posterior Expectations in Elliptic Inverse Problems. SIAM/ASA Journal on Uncertainty Quantification, 5(1), 493–518, 2017. Available as arXiv preprint arXiv:1602.04704.
- T.J. Dodwell, C. Ketelsen, R. Scheichl, A.L. Teckentrup. A Hierarchical Multilevel Markov Chain Monte Carlo Algorithm with Applications to Uncertainty Quantification in Subsurface Flow. SIAM/ASA Journal on Uncertainty Quantification 3(1), 1075-1108, 2015. Available as arXiv preprint arXiv:1303:7343. Erratum
- A.L. Teckentrup, P. Jantsch, C.G. Webster, M. Gunzburger. A Multilevel Stochastic Collocation Method for Partial Differential Equations with Random Input Data. SIAM/ASA Journal on Uncertainty Quantification, 3(1), 1046–1074, 2015. Available as arXiv preprint arXiv:1404.2647.
- A.L. Teckentrup, R. Scheichl, M.B. Giles, E. Ullmann. Further analysis of multilevel Monte Carlo methods for elliptic PDEs with random coefficients. Numerische Mathematik, 125(3), 569-600, 2013. Available as arXiv preprint arXiv:1204:3476.
- J. Charrier, R.Scheichl, A.L. Teckentrup. Finite Element Error Analysis of Elliptic PDEs with Random Coefficients and its Application to Multilevel Monte Carlo Methods. SIAM Journal on Numerical Analysis, 51(1), 322-352, 2013.
- K.A. Cliffe, M.B. Giles, R. Scheichl, A.L. Teckentrup. Multilevel Monte Carlo Methods and Applications to Elliptic PDEs with Random Coefficients. Computing and Visualization in Science, 14(1), 3-15, 2011.
- T. Helin, A.M. Stuart, A.L. Teckentrup, K.C. Zygalakis. Introduction To Gaussian Process Regression In Bayesian Inverse Problems, With New Results On Experimental Design For Weighted Error Measures. Available as arXiv preprint arXiv:2302.04518. To appear in: A. Hinrichs, P. Kritzer, F. Pillichshammer (eds.). Monte Carlo and Quasi-Monte Carlo Methods 2022. Springer Verlag.
- H.C. Lie, T.J. Sullivan, A.L. Teckentrup. Error bounds for some approximate posterior measures in Bayesian inference. Numerical Mathematics and Advanced Applications ENUMATH 2019, Springer, 2021.
- M.A. Fisher, C.J. Oates, C.E. Powell and A.L. Teckentrup. A locally adaptive Bayesian cubature method. In International Conference on Artificial Intelligence and Statistics (AISTATS), Proceedings of Machine Learning Research, p. 1265-1275, 2020.
- A.L.Teckentrup. Multilevel Monte Carlo Methods for Highly Heterogeneous Media. Winter Simulation Conference '12 proceedings, available at https://ieeexplore.ieee.org/document/6465298.
- A.L. Teckentrup. Multilevel Monte Carlo methods and uncertainty quantification. PhD thesis, University of Bath (UK), June 2013. PDF available online here.
- A.L. Teckentrup, M.M. Dunlop, M. Girolami, A.M. Stuart. Deep Gaussian processes and applications in Bayesian inverse problems, Oberwolfach Report 35, 2019.
- A.L. Teckentrup, H.C. Lie, A.M. Stuart, T.J. Sullivan. Computational approximations in Bayesian inverse problems. Oberwolfach Report 12, 2019.
- A.L. Teckentrup, A.M. Stuart. Gaussian process regression in Bayesian inverse problems, Dagstuhl Reports, 6(9), 59-73, 2017.
- M. Gunzburger, P. Jantsch, A.L. Teckentrup, C.G. Webster. A multilevel stochastic collocation method for SPDEs. AIP Conference Proceedings, 1648(1), p. 020005, 2015.
- M. Park, A.L. Teckentrup. Improved Multilevel Monte Carlo Methods for Finite Volume Discretisations of Darcy Flow in Randomly Layered Media. Technical report. Available as arXiv preprint arXiv:1506.04694.
- M. Gunzburger, A.L. Teckentrup. Computing Approximate Optimal Point Sets for Total Degree Polynomial Interpolation in Moderate Dimensions. Technical report. Available as arXiv preprint arXiv:1407.3291. Interpolation points computed in this paper are available in the MATLAB scripts points_cube.m (for the unit cube) and points_ball.m (for the unit ball).