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…
Error bounds for some approximate posterior measures in Bayesian inference
Han Cheng Lie, T. J. Sullivan, Aretha Teckentrup
In certain applications involving the solution of a Bayesian inverse problem, it may not be possible or desirable to evaluate the full posterior, e.g. due to the high computational…
Random forward models and log-likelihoods in Bayesian inverse problems
H. C. Lie, T. J. Sullivan, A. L. Teckentrup
We consider the use of randomised forward models and log-likelihoods within the Bayesian approach to inverse problems. Such random approximations to the exact forward model or log-…
How Deep Are Deep Gaussian Processes?
Matthew M. Dunlop, Mark A. Girolami, Andrew M. Stuart +1
Recent research has shown the potential utility of Deep Gaussian Processes. These deep structures are probability distributions, designed through hierarchical construction, which a…