5 papers
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…
Black Box Probabilistic Numerics
Onur Teymur, Christopher N. Foley, Philip G. Breen +2
Probabilistic numerics casts numerical tasks, such the numerical solution of differential equations, as inference problems to be solved. One approach is to model the unknown quanti…
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…
Optimal quantisation of probability measures using maximum mean discrepancy
Onur Teymur, Jackson Gorham, Marina Riabiz +1
Several researchers have proposed minimisation of maximum mean discrepancy (MMD) as a method to quantise probability measures, i.e., to approximate a target distribution by a repre…
A Bayesian nonparametric test for conditional independence
Onur Teymur, Sarah Filippi
This article introduces a Bayesian nonparametric method for quantifying the relative evidence in a dataset in favour of the dependence or independence of two variables conditional…