3 citations · 6 across the 8 of their papers we have counts for
6 papers · 1 filter
Post-Processing of MCMC
Leah F. South, Marina Riabiz, Onur Teymur +1
Markov chain Monte Carlo (MCMC) is the engine of modern Bayesian statistics, being used to approximate the posterior and derived quantities of interest. Despite this, the issue of…
Probabilistic Iterative Methods for Linear Systems
Jon Cockayne, Ilse C. F. Ipsen, Chris J. Oates +1
This paper presents a probabilistic perspective on iterative methods for approximating the solution of a nonsingular linear system $\mathbf{A} \math…
Discussion of "Unbiased Markov chain Monte Carlo with couplings" by Pierre E. Jacob, John O'Leary and Yves F. Atchadé
Leah F. South, Chris Nemeth, Chris J. Oates
This is a contribution for the discussion on "Unbiased Markov chain Monte Carlo with couplings" by Pierre E. Jacob, John O'Leary and Yves F. Atchadé to appear in the Journal of the…
Optimality Criteria for Probabilistic Numerical Methods
Chris. J. Oates, Jon Cockayne, Dennis Prangle +2
It is well understood that Bayesian decision theory and average case analysis are essentially identical. However, if one is interested in performing uncertainty quantification for…
On the Bayesian Solution of Differential Equations
Junyang Wang, Jon Cockayne, Chris Oates
The interpretation of numerical methods, such as finite difference methods for differential equations, as point estimators allows for formal statistical quantification of the error…
Discussion of "Causal inference using invariant prediction: identification and confidence intervals" by Peters, Bühlmann and Meinshausen
Chris J. Oates, Jessica Kasza, Sach Mukherjee
Contribution to the discussion of the paper "Causal inference using invariant prediction: identification and confidence intervals" by Peters, Bühlmann and Meinshausen, to appear in…