3 citations · 7 across the 12 of their papers we have counts for
9 papers · 1 filter
Harnessing the Power of Reinforcement Learning for Adaptive MCMC
Congye Wang, Matthew A. Fisher, Heishiro Kanagawa +2
Sampling algorithms drive probabilistic machine learning, and recent years have seen an explosion in the diversity of tools for this task. However, the increasing sophistication of…
Fast Approximate Solution of Stein Equations for Post-Processing of MCMC
Qingyang Liu, Heishiro Kanagawa, Matthew A. Fisher +2
Bayesian inference is conceptually elegant, but calculating posterior expectations can entail a heavy computational cost. Monte Carlo methods are reliable and supported by strong a…
Grand Challenges in Bayesian Computation
Anirban Bhattacharya, Antonio Linero, Chris. J. Oates
This article appeared in the September 2024 issue (Vol. 31, No. 3) of the Bulletin of the International Society for Bayesian Analysis (ISBA).
GaussED: A Probabilistic Programming Language for Sequential Experimental Design
Matthew A. Fisher, Onur Teymur, Chris. J. Oates
Sequential algorithms are popular for experimental design, enabling emulation, optimisation and inference to be efficiently performed. For most of these applications bespoke softwa…
Minimum Discrepancy Methods in Uncertainty Quantification
Chris J. Oates
The lectures were prepared for the École Thématique sur les Incertitudes en Calcul Scientifique (ETICS) in September 2021.
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…