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05/17/2023
Stein Î -Importance Sampling
Stein discrepancies have emerged as a powerful tool for retrospective im...
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03/08/2023
Meta-learning Control Variates: Variance Reduction with Limited Data
Control variates can be a powerful tool to reduce the variance of Monte ...
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03/05/2023
The Matérn Model: A Journey through Statistics, Numerical Analysis and Machine Learning
The Matérn model has been a cornerstone of spatial statistics for more t...
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11/16/2022
Sobolev Spaces, Kernels and Discrepancies over Hyperspheres
This work provides theoretical foundations for kernel methods in the hyp...
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10/28/2022
Minimum Kernel Discrepancy Estimators
For two decades, reproducing kernels and their associated discrepancies ...
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08/08/2022
Statistical Properties of the Probabilistic Numeric Linear Solver BayesCG
We analyse the calibration of BayesCG under the Krylov prior, a probabil...
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07/06/2022
Gradient-Free Kernel Stein Discrepancy
Stein discrepancies have emerged as a powerful statistical tool, being a...
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06/16/2022
Generalised Bayesian Inference for Discrete Intractable Likelihood
Discrete state spaces represent a major computational challenge to stati...
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03/17/2022
Maximum Likelihood Estimation in Gaussian Process Regression is Ill-Posed
Gaussian process regression underpins countless academic and industrial ...
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11/15/2021
A Statistical Approach to Surface Metrology for 3D-Printed Stainless Steel
Surface metrology is the area of engineering concerned with the study of...
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10/15/2021
GaussED: A Probabilistic Programming Language for Sequential Experimental Design
Sequential algorithms are popular for experimental design, enabling emul...
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09/13/2021
Minimum Discrepancy Methods in Uncertainty Quantification
The lectures were prepared for the École Thématique sur les Incertitudes...
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06/15/2021
Black Box Probabilistic Numerics
Probabilistic numerics casts numerical tasks, such the numerical solutio...
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05/07/2021
Stein's Method Meets Statistics: A Review of Some Recent Developments
Stein's method is a collection of tools for analysing distributional com...
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04/22/2021
Bayesian Numerical Methods for Nonlinear Partial Differential Equations
The numerical solution of differential equations can be formulated as an...
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04/15/2021
Robust Generalised Bayesian Inference for Intractable Likelihoods
Generalised Bayesian inference updates prior beliefs using a loss functi...
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03/30/2021
Post-Processing of MCMC
Markov chain Monte Carlo (MCMC) is the engine of modern Bayesian statist...
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12/23/2020
Testing whether a Learning Procedure is Calibrated
A learning procedure takes as input a dataset and performs inference for...
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12/23/2020
Probabilistic Iterative Methods for Linear Systems
This paper presents a probabilistic perspective on iterative methods for...
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10/22/2020
Measure Transport with Kernel Stein Discrepancy
Measure transport underpins several recent algorithms for posterior appr...
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10/16/2020
The Ridgelet Prior: A Covariance Function Approach to Prior Specification for Bayesian Neural Networks
Bayesian neural networks attempt to combine the strong predictive perfor...
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10/14/2020
Optimal quantisation of probability measures using maximum mean discrepancy
Several researchers have proposed minimisation of maximum mean discrepan...
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08/07/2020
A Probabilistic Numerical Extension of the Conjugate Gradient Method
We present a Conjugate Gradient (CG) implementation of the probabilistic...
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06/12/2020
Scalable Control Variates for Monte Carlo Methods via Stochastic Optimization
Control variates are a well-established tool to reduce the variance of M...
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05/08/2020
Optimal Thinning of MCMC Output
The use of heuristics to assess the convergence and compress the output ...
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04/27/2020
Integration in reproducing kernel Hilbert spaces of Gaussian kernels
The Gaussian kernel plays a central role in machine learning, uncertaint...
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01/31/2020
Semi-Exact Control Functionals From Sard's Method
This paper focuses on the numerical computation of posterior expected qu...
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01/29/2020
Maximum likelihood estimation and uncertainty quantification for Gaussian process approximation of deterministic functions
Despite the ubiquity of the Gaussian process regression model, few theor...
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12/22/2019
Discussion of "Unbiased Markov chain Monte Carlo with couplings" by Pierre E. Jacob, John O'Leary and Yves F. Atchadé
This is a contribution for the discussion on "Unbiased Markov chain Mont...
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10/07/2019
A Locally Adaptive Bayesian Cubature Method
Bayesian cubature (BC) is a popular inferential perspective on the cubat...
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06/24/2019
A Role for Symmetry in the Bayesian Solution of Differential Equations
The interpretation of numerical methods, such as finite difference metho...
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05/09/2019
Stein Point Markov Chain Monte Carlo
An important task in machine learning and statistics is the approximatio...
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01/14/2019
Optimality Criteria for Probabilistic Numerical Methods
It is well understood that Bayesian decision theory and average case ana...
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11/28/2018
Improved Calibration of Numerical Integration Error in Sigma-Point Filters
The sigma-point filters, such as the UKF, which exploit numerical quadra...
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11/26/2018
Rejoinder for "Probabilistic Integration: A Role in Statistical Computation?"
This article is the rejoinder for the paper "Probabilistic Integration: ...
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11/13/2018
Regularised Zero-Variance Control Variates for High-Dimensional Variance Reduction
Zero-variance control variates (ZV-CV) are a post-processing method to r...
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10/11/2018
A Riemannian-Stein Kernel Method
This paper presents a theoretical analysis of numerical integration base...
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09/26/2018
Symmetry Exploits for Bayesian Cubature Methods
Bayesian cubature provides a flexible framework for numerical integratio...
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04/09/2018
A Bayes-Sard Cubature Method
This paper focusses on the formulation of numerical integration as an in...
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03/27/2018
Stein Points
An important task in computational statistics and machine learning is to...
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12/05/2017
Posterior Integration on a Riemannian Manifold
The geodesic Markov chain Monte Carlo method and its variants enable com...
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12/05/2017
Posterior Integration on an Embedded Riemannian Manifold
This note extends the posterior integration method of Oates et al. (2016...
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06/11/2017
On the Sampling Problem for Kernel Quadrature
The standard Kernel Quadrature method for numerical integration with ran...
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12/03/2015
Probabilistic Integration: A Role in Statistical Computation?
A research frontier has emerged in scientific computation, wherein numer...
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06/08/2015
Frank-Wolfe Bayesian Quadrature: Probabilistic Integration with Theoretical Guarantees
There is renewed interest in formulating integration as an inference pro...
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04/04/2014
