140 citations · 1.9k across the 86 of their papers we have counts for
8 papers · 1 filter
Hyperpriors for Matérn fields with applications in Bayesian inversion
Lassi Roininen, Mark Girolami, Sari Lasanen +1
We introduce non-stationary Matérn field priors with stochastic partial differential equations, and construct correlation length-scaling with hyperpriors. We model both the hyperpr…
Probabilistic Models for Integration Error in the Assessment of Functional Cardiac Models
Chris. J. Oates, Steven Niederer, Angela Lee +2
This paper studies the numerical computation of integrals, representing estimates or predictions, over the output of a computational model with respect to a distribution $p(…
Geometric MCMC for Infinite-Dimensional Inverse Problems
Alexandros Beskos, Mark Girolami, Shiwei Lan +2
Bayesian inverse problems often involve sampling posterior distributions on infinite-dimensional function spaces. Traditional Markov chain Monte Carlo (MCMC) algorithms are charact…
Probabilistic Numerical Methods for Partial Differential Equations and Bayesian Inverse Problems
Jon Cockayne, Chris Oates, Tim Sullivan +1
This paper develops a probabilistic numerical method for solution of partial differential equations (PDEs) and studies application of that method to PDE-constrained inverse problem…
Bayesian modelling and quantification of Raman spectroscopy
Matthew Moores, Kirsten Gracie, Jake Carson +3
Raman spectroscopy can be used to identify molecules such as DNA by the characteristic scattering of light from a laser. It is sensitive at very low concentrations and can accurate…
Unbiased local solutions of partial differential equations via the Feynman-Kac Identities
Jake Carson, Murray Pollock, Mark Girolami
The Feynman-Kac formulae (FKF) express local solutions of partial differential equations (PDEs) as expectations with respect to some complementary stochastic differential equation…