21 citations · 63 across the 9 of their papers we have counts for
9 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…
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…
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…
Implicit Probabilistic Integrators for ODEs
Onur Teymur, Han Cheng Lie, Tim Sullivan +1
We introduce a family of implicit probabilistic integrators for initial value problems (IVPs), taking as a starting point the multistep Adams-Moulton method. The implicit construct…
Probabilistic Linear Multistep Methods
Onur Teymur, Konstantinos Zygalakis, Ben Calderhead
We present a derivation and theoretical investigation of the Adams-Bashforth and Adams-Moulton family of linear multistep methods for solving ordinary differential equations, start…
Comments on "Bayesian Solution Uncertainty Quantification for Differential Equations" by Chkrebtii, Campbell, Calderhead & Girolami
Francois-Xavier Briol, Jon Cockayne, Onur Teymur
We commend the authors for an exciting paper which provides a strong contribution to the emerging field of probabilistic numerics (PN). Below, we discuss aspects of prior modelling…