Integration against, and hence sampling from, high-dimensional probabili...
The last decade has seen many attempts to generalise the definition of m...
Gaussian processes (GPs) are widely-used tools in spatial statistics and...
We consider the problem of learning a linear operator θ between two
Hilb...
Stochastic parareal (SParareal) is a probabilistic variant of the popula...
It is often desirable to summarise a probability measure on a space X in...
Sequential numerical methods for integrating initial value problems (IVP...
We consider Bayesian analysis on high-dimensional spheres with angular
c...
We derive Onsager-Machlup functionals for countable product measures on
...
The Bayesian solution to a statistical inverse problem can be summarised...
The numerical solution of differential equations can be formulated as an...
Uncertainty quantification plays an important role in applications that
...
A learning procedure takes as input a dataset and performs inference for...
We demonstrate that the recently developed Optimal Uncertainty Quantific...
The linear conditional expectation (LCE) provides a best linear (or rath...
Motivated by the desire to numerically calculate rigorous upper and lowe...
Conditional mean embeddings (CME) have proven themselves to be a powerfu...
In certain applications involving the solution of a Bayesian inverse pro...
We analytically determine Jacobi fields and parallel transports and comp...
The recent article "A Bayesian conjugate gradient method" by Cockayne, O...
This article attempts to cast the emergence of probabilistic numerics as...
It is well understood that Bayesian decision theory and average case ana...
A recently-introduced class of probabilistic (uncertainty-aware) solvers...
We consider the use of randomised forward models and log-likelihoods wit...
We consider the application of active subspaces to inform a
Metropolis-H...
The Bayesian perspective on inverse problems has attracted much mathemat...