23 citations · 52 across the 9 of their papers we have counts for
10 papers
Parameter Inference for Degenerate Diffusion Processes
Yuga Iguchi, Alexandros Beskos, Matthew Graham
We study parametric inference for ergodic diffusion processes with a degenerate diffusion matrix. Existing research focuses on a particular class of hypo-elliptic SDEs, with compon…
Parameter Estimation with Increased Precision for Elliptic and Hypo-elliptic Diffusions
Yuga Iguchi, Alexandros Beskos, Matthew M. Graham
This work aims at making a comprehensive contribution in the general area of parametric inference for discretely observed diffusion processes. Established approaches for likelihood…
Testing whether a Learning Procedure is Calibrated
Jon Cockayne, Matthew M. Graham, Chris J. Oates +2
A learning procedure takes as input a dataset and performs inference for the parameters of a model that is assumed to have given rise to the dataset. Here we consider learning…
Measure Transport with Kernel Stein Discrepancy
Matthew A. Fisher, Tui Nolan, Matthew M. Graham +2
Measure transport underpins several recent algorithms for posterior approximation in the Bayesian context, wherein a transport map is sought to minimise the Kullback--Leibler diver…
Manifold lifting: scaling MCMC to the vanishing noise regime
Khai Xiang Au, Matthew M. Graham, Alexandre H. Thiery
Standard Markov chain Monte Carlo methods struggle to explore distributions that are concentrated in the neighbourhood of low-dimensional structures. These pathologies naturally oc…
Manifold Markov chain Monte Carlo methods for Bayesian inference in diffusion models
Matthew M. Graham, Alexandre H. Thiery, Alexandros Beskos
Bayesian inference for nonlinear diffusions, observed at discrete times, is a challenging task that has prompted the development of a number of algorithms, mainly within the comput…
A scalable optimal-transport based local particle filter
Matthew M. Graham, Alexandre H. Thiery
Filtering in spatially-extended dynamical systems is a challenging problem with significant practical applications such as numerical weather prediction. Particle filters allow asym…
Continuously tempered Hamiltonian Monte Carlo
Matthew M. Graham, Amos J. Storkey
Hamiltonian Monte Carlo (HMC) is a powerful Markov chain Monte Carlo (MCMC) method for performing approximate inference in complex probabilistic models of continuous variables. In…
Asymptotically exact inference in differentiable generative models
Matthew M. Graham, Amos J. Storkey
Many generative models can be expressed as a differentiable function of random inputs drawn from some simple probability density. This framework includes both deep generative archi…
Pseudo-Marginal Slice Sampling
Iain Murray, Matthew M. Graham
Markov chain Monte Carlo (MCMC) methods asymptotically sample from complex probability distributions. The pseudo-marginal MCMC framework only requires an unbiased estimator of the…