41 citations · 165 across the 24 of their papers we have counts for
25 papers
A computationally-tractable measure of global sensitivity for sampling-based Bayesian inference
Arina Odnoblyudova, Charita Dellaporta, François-Xavier Briol
Bayesian inference can often be sensitive to the choice of hyperparameters of the prior or likelihood, yet defining and quantifying this sensitivity in a principled and computation…
Conjugate Generalized Bayesian Inference for Discrete Doubly Intractable Problems
William Laplante, Matias Altamirano, Jeremias Knoblauch +2
Doubly intractable problems occur when both the likelihood and the posterior are available only in unnormalized form, with computationally intractable normalization constants. Baye…
Kernel Quantile Embeddings and Associated Probability Metrics
Masha Naslidnyk, Siu Lun Chau, François-Xavier Briol +1
Embedding probability distributions into reproducing kernel Hilbert spaces (RKHS) has enabled powerful nonparametric methods such as the maximum mean discrepancy (MMD), a statistic…
On the Robustness of Kernel Goodness-of-Fit Tests
Xing Liu, François-Xavier Briol
Goodness-of-fit testing is often criticized for its lack of practical relevance: since ``all models are wrong'', the null hypothesis that the data conform to our model is ultimatel…
Outlier-robust Kalman Filtering through Generalised Bayes
Gerardo Duran-Martin, Matias Altamirano, Alexander Y. Shestopaloff +5
We derive a novel, provably robust, and closed-form Bayesian update rule for online filtering in state-space models in the presence of outliers and misspecified measurement models.…
Robust and Conjugate Gaussian Process Regression
Matias Altamirano, François-Xavier Briol, Jeremias Knoblauch
To enable closed form conditioning, a common assumption in Gaussian process (GP) regression is independent and identically distributed Gaussian observation noise. This strong and s…
Data-driven modelling of turbine wake interactions and flow resistance in large wind farms
Andrew Kirby, François-Xavier Briol, Thomas D. Dunstan +1
Turbine wake and local blockage effects are known to alter wind farm power production in two different ways: (1) by changing the wind speed locally in front of each turbine; and (2…
Generalised Bayesian Inference for Discrete Intractable Likelihood
Takuo Matsubara, Jeremias Knoblauch, François-Xavier Briol +1
Discrete state spaces represent a major computational challenge to statistical inference, since the computation of normalisation constants requires summation over large or possibly…
Robust Generalised Bayesian Inference for Intractable Likelihoods
Takuo Matsubara, Jeremias Knoblauch, François-Xavier Briol +1
Generalised Bayesian inference updates prior beliefs using a loss function, rather than a likelihood, and can therefore be used to confer robustness against possible mis-specificat…
A General Method for Calibrating Stochastic Radio Channel Models with Kernels
Ayush Bharti, Francois-Xavier Briol, Troels Pedersen
Calibrating stochastic radio channel models to new measurement data is challenging when the likelihood function is intractable. The standard approach to this problem involves sophi…
Joint Modeling of Received Power, Mean Delay, and Delay Spread for Wideband Radio Channels
Ayush Bharti, Ramoni Adeogun, Xuesong Cai +4
We propose a multivariate log-normal distribution to jointly model received power, mean delay, and root mean square (rms) delay spread of wideband radio channels, referred to as th…
Contributed Discussion of "A Bayesian Conjugate Gradient Method"
Francois-Xavier Briol, Francisco A. Diaz De la O, Peter O. Hristov
We would like to congratulate the authors of "A Bayesian Conjugate Gradient Method" on their insightful paper, and welcome this publication which we firmly believe will become a fu…
Statistical Inference for Generative Models with Maximum Mean Discrepancy
Francois-Xavier Briol, Alessandro Barp, Andrew B. Duncan +1
While likelihood-based inference and its variants provide a statistically efficient and widely applicable approach to parametric inference, their application to models involving in…
Minimum Stein Discrepancy Estimators
Alessandro Barp, Francois-Xavier Briol, Andrew B. Duncan +2
When maximum likelihood estimation is infeasible, one often turns to score matching, contrastive divergence, or minimum probability flow to obtain tractable parameter estimates. We…
Stein Point Markov Chain Monte Carlo
Wilson Ye Chen, Alessandro Barp, François-Xavier Briol +4
An important task in machine learning and statistics is the approximation of a probability measure by an empirical measure supported on a discrete point set. Stein Points are a cla…
Rejoinder for "Probabilistic Integration: A Role in Statistical Computation?"
Francois-Xavier Briol, Chris J. Oates, Mark Girolami +2
This article is the rejoinder for the paper "Probabilistic Integration: A Role in Statistical Computation?" to appear in Statistical Science with discussion. We would first like to…
Stein Points
Wilson Ye Chen, Lester Mackey, Jackson Gorham +2
An important task in computational statistics and machine learning is to approximate a posterior distribution with an empirical measure supported on a set of representative…
Bayesian Quadrature for Multiple Related Integrals
Xiaoyue Xi, François-Xavier Briol, Mark Girolami
Bayesian probabilistic numerical methods are a set of tools providing posterior distributions on the output of numerical methods. The use of these methods is usually motivated by t…
On the Sampling Problem for Kernel Quadrature
Francois-Xavier Briol, Chris J. Oates, Jon Cockayne +2
The standard Kernel Quadrature method for numerical integration with random point sets (also called Bayesian Monte Carlo) is known to converge in root mean square error at a rate d…
Geometry and Dynamics for Markov Chain Monte Carlo
Alessandro Barp, Francois-Xavier Briol, Anthony D. Kennedy +1
Markov Chain Monte Carlo methods have revolutionised mathematical computation and enabled statistical inference within many previously intractable models. In this context, Hamilton…
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…
Probabilistic Models for Integration Error in the Assessment of Functional Cardiac Models
Chris. J. Oates, Steven Niederer, Angela Lee +2
This paper studies the numerical computation of integrals, representing estimates or predictions, over the output of a computational model with respect to a distribution $p(…
Probabilistic Integration: A Role in Statistical Computation?
François-Xavier Briol, Chris. J. Oates, Mark Girolami +2
A research frontier has emerged in scientific computation, wherein numerical error is regarded as a source of epistemic uncertainty that can be modelled. This raises several statis…
Frank-Wolfe Bayesian Quadrature: Probabilistic Integration with Theoretical Guarantees
François-Xavier Briol, Chris J. Oates, Mark Girolami +1
There is renewed interest in formulating integration as an inference problem, motivated by obtaining a full distribution over numerical error that can be propagated through subsequ…