Mark Girolami

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University of Cambridge ∙ Imperial College London ∙ University of Warwick

LRF/RAEng Research Chair in Data Centric Engineering

Continuous calibration of a digital twin: comparison of particle filter and Bayesian calibration approaches

Assimilation of continuously streamed monitored data is an essential com...
11/19/2020 ∙ by Rebecca Ward, et al. ∙ 0 ∙ share

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Integration in reproducing kernel Hilbert spaces of Gaussian kernels

The Gaussian kernel plays a central role in machine learning, uncertaint...
04/27/2020 ∙ by Toni Karvonen, et al. ∙ 0 ∙ share

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Semi-Exact Control Functionals From Sard's Method

This paper focuses on the numerical computation of posterior expected qu...
01/31/2020 ∙ by Leah F. South, et al. ∙ 0 ∙ share

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Convergence Guarantees for Gaussian Process Approximations Under Several Observation Models

Gaussian processes are ubiquitous in statistical analysis, machine learn...
01/29/2020 ∙ by George Wynne, et al. ∙ 34 ∙ share

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Burglary in London: Insights from Statistical Heterogeneous Spatial Point Processes

To obtain operational insights regarding the crime of burglary in London...
10/11/2019 ∙ by Jan Povala, et al. ∙ 0 ∙ share

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Embedded Ridge Approximations: Constructing Ridge Approximations Over Localized Scalar Fields For Improved Simulation-Centric Dimension Reduction

Many quantities of interest (qois) arising from differential-equation-ce...
07/16/2019 ∙ by Chun Yui Wong, et al. ∙ 0 ∙ share

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Multi-resolution Multi-task Gaussian Processes

We consider evidence integration from potentially dependent observation ...
06/19/2019 ∙ by Oliver Hamelijnck, et al. ∙ 4 ∙ share

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Minimum Stein Discrepancy Estimators

When maximum likelihood estimation is infeasible, one often turns to sco...
06/19/2019 ∙ by Alessandro Barp, et al. ∙ 2 ∙ share

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Statistical Inference for Generative Models with Maximum Mean Discrepancy

While likelihood-based inference and its variants provide a statisticall...
06/13/2019 ∙ by Francois-Xavier Briol, et al. ∙ 0 ∙ share

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The Statistical Finite Element Method

The finite element method (FEM) is one of the great triumphs of modern d...
05/15/2019 ∙ by Mark Girolami, et al. ∙ 0 ∙ share

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Stein Point Markov Chain Monte Carlo

An important task in machine learning and statistics is the approximatio...
05/09/2019 ∙ by Wilson Ye Chen, et al. ∙ 12 ∙ share

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Hamiltonian Monte Carlo on Symmetric and Homogeneous Spaces via Symplectic Reduction

The Hamiltonian Monte Carlo method generates samples by introducing a me...
03/07/2019 ∙ by Alessandro Barp, et al. ∙ 0 ∙ share

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Optimality Criteria for Probabilistic Numerical Methods

It is well understood that Bayesian decision theory and average case ana...
01/14/2019 ∙ by Chris J. Oates, et al. ∙ 0 ∙ share

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Efficiency and robustness in Monte Carlo sampling of 3-D geophysical inversions with Obsidian v0.1.2: Setting up for success

The rigorous quantification of uncertainty in geophysical inversions is ...
12/02/2018 ∙ by Richard Scalzo, et al. ∙ 0 ∙ share

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The synthesis of data from instrumented structures and physics-based models via Gaussian processes

A recent development which is poised to disrupt current structural engin...
11/27/2018 ∙ by Alastair Gregory, et al. ∙ 0 ∙ share

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Rejoinder for "Probabilistic Integration: A Role in Statistical Computation?"

This article is the rejoinder for the paper "Probabilistic Integration: ...
11/26/2018 ∙ by Francois-Xavier Briol, et al. ∙ 18 ∙ share

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A Riemannian-Stein Kernel Method

This paper presents a theoretical analysis of numerical integration base...
10/11/2018 ∙ by Alessandro Barp, et al. ∙ 0 ∙ share

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Probabilistic Linear Solvers: A Unifying View

Several recent works have developed a new, probabilistic interpretation ...
10/08/2018 ∙ by Simon Bartels, et al. ∙ 0 ∙ share

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Posterior Inference for Sparse Hierarchical Non-stationary Models

Gaussian processes are valuable tools for non-parametric modelling, wher...
04/04/2018 ∙ by Karla Monterrubio-Gómez, et al. ∙ 0 ∙ share

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A Bayesian Conjugate Gradient Method

A fundamental task in numerical computation is the solution of large lin...
01/16/2018 ∙ by Jon Cockayne, et al. ∙ 0 ∙ share

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Bayesian Quadrature for Multiple Related Integrals

Bayesian probabilistic numerical methods are a set of tools providing po...
01/12/2018 ∙ by Xiaoyue Xi, et al. ∙ 0 ∙ share

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Posterior Integration on a Riemannian Manifold

The geodesic Markov chain Monte Carlo method and its variants enable com...
12/05/2017 ∙ by Chris J. Oates, et al. ∙ 0 ∙ share

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How Deep Are Deep Gaussian Processes?

Recent research has shown the potential utility of probability distribut...
11/30/2017 ∙ by Matthew M. Dunlop, et al. ∙ 0 ∙ share

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A determinant-free method to simulate the parameters of large Gaussian fields

We propose a determinant-free approach for simulation-based Bayesian inf...
09/11/2017 ∙ by Louis Ellam, et al. ∙ 0 ∙ share

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On the Sampling Problem for Kernel Quadrature

The standard Kernel Quadrature method for numerical integration with ran...
06/11/2017 ∙ by Francois-Xavier Briol, et al. ∙ 0 ∙ share

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Geometry and Dynamics for Markov Chain Monte Carlo

Markov Chain Monte Carlo methods have revolutionised mathematical comput...
05/08/2017 ∙ by Alessandro Barp, et al. ∙ 0 ∙ share

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Bayesian Probabilistic Numerical Methods

The emergent field of probabilistic numerics has thus far lacked clear s...
02/13/2017 ∙ by Jon Cockayne, et al. ∙ 0 ∙ share

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Probabilistic Numerical Methods for PDE-constrained Bayesian Inverse Problems

This paper develops meshless methods for probabilistically describing di...
01/15/2017 ∙ by Jon Cockayne, et al. ∙ 0 ∙ share

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On the Geometric Ergodicity of Hamiltonian Monte Carlo

We establish general conditions under which Markov chains produced by th...
01/29/2016 ∙ by Samuel Livingstone, et al. ∙ 0 ∙ share

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Probabilistic Integration: A Role in Statistical Computation?

A research frontier has emerged in scientific computation, wherein numer...
12/03/2015 ∙ by Francois-Xavier Briol, et al. ∙ 0 ∙ share

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Frank-Wolfe Bayesian Quadrature: Probabilistic Integration with Theoretical Guarantees

There is renewed interest in formulating integration as an inference pro...
06/08/2015 ∙ by Francois-Xavier Briol, et al. ∙ 0 ∙ share

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Probabilistic Numerics and Uncertainty in Computations

We deliver a call to arms for probabilistic numerical methods: algorithm...
06/03/2015 ∙ by Philipp Hennig, et al. ∙ 0 ∙ share

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Unbiased Bayes for Big Data: Paths of Partial Posteriors

A key quantity of interest in Bayesian inference are expectations of fun...
01/14/2015 ∙ by Heiko Strathmann, et al. ∙ 0 ∙ share

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Pseudo-Marginal Bayesian Inference for Gaussian Processes

The main challenges that arise when adopting Gaussian Process priors in ...
10/02/2013 ∙ by Maurizio Filippone, et al. ∙ 0 ∙ share

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On Russian Roulette Estimates for Bayesian Inference with Doubly-Intractable Likelihoods

A large number of statistical models are "doubly-intractable": the likel...
06/17/2013 ∙ by Anne-Marie Lyne, et al. ∙ 0 ∙ share

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Mark Girolami

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