140 citations · 1.9k across the 86 of their papers we have counts for
7 papers · 1 filter
Posterior Integration on a Riemannian Manifold
Chris. J. Oates, Alessandro Barp, Mark Girolami
The geodesic Markov chain Monte Carlo method and its variants enable computation of integrals with respect to a posterior supported on a manifold. However, for regular integrals, t…
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…
A determinant-free method to simulate the parameters of large Gaussian fields
Louis Ellam, Heiko Strathmann, Mark Girolami +1
We propose a determinant-free approach for simulation-based Bayesian inference in high-dimensional Gaussian models. We introduce auxiliary variables with covariance equal to the in…
On the Sampling Problem for Kernel Quadrature
Francois-Xavier Briol, Chris J. Oates, Jon Cockayne +2
The standard Kernel Quadrature method for numerical integration with random point sets (also called Bayesian Monte Carlo) is known to converge in root mean square error at a rate d…
Geometry and Dynamics for Markov Chain Monte Carlo
Alessandro Barp, Francois-Xavier Briol, Anthony D. Kennedy +1
Markov Chain Monte Carlo methods have revolutionised mathematical computation and enabled statistical inference within many previously intractable models. In this context, Hamilton…
Bayesian Probabilistic Numerical Methods
Jon Cockayne, Chris Oates, Tim Sullivan +1
The emergent field of probabilistic numerics has thus far lacked clear statistical principals. This paper establishes Bayesian probabilistic numerical methods as those which can be…