18 citations · 80 across the 22 of their papers we have counts for
4 papers · 1 filter
Convergence rates of non-stationary and deep Gaussian process regression
Conor Osborne, Aretha L. Teckentrup
The focus of this work is the convergence of non-stationary and deep Gaussian process regression. More precisely, we follow a Bayesian approach to regression or interpolation, wher…
Gaussian processes for Bayesian inverse problems associated with linear partial differential equations
Tianming Bai, Aretha L. Teckentrup, Konstantinos C. Zygalakis
This work is concerned with the use of Gaussian surrogate models for Bayesian inverse problems associated with linear partial differential equations. A particular focus is on the r…
Smoothed Circulant Embedding with Applications to Multilevel Monte Carlo Methods for PDEs with Random Coefficients
Anastasia Istratuca, Aretha Teckentrup
We consider the computational efficiency of Monte Carlo (MC) and Multilevel Monte Carlo (MLMC) methods applied to partial differential equations with random coefficients. These ari…
Introduction To Gaussian Process Regression In Bayesian Inverse Problems, With New ResultsOn Experimental Design For Weighted Error Measures
Tapio Helin, Andrew Stuart, Aretha Teckentrup +1
Bayesian posterior distributions arising in modern applications, including inverse problems in partial differential equation models in tomography and subsurface flow, are often com…