Probabilistic integration
A research frontier has emerged in scientific computation, wherein discretisation error is
regarded as a source of epistemic uncertainty that can be modelled. This raises several …
regarded as a source of epistemic uncertainty that can be modelled. This raises several …
A big data analysis approach for rail failure risk assessment
Railway infrastructure monitoring is a vital task to ensure rail transportation safety. A rail
failure could result in not only a considerable impact on train delays and maintenance costs …
failure could result in not only a considerable impact on train delays and maintenance costs …
A modern retrospective on probabilistic numerics
This article attempts to place the emergence of probabilistic numerics as a mathematical–
statistical research field within its historical context and to explore how its gradual …
statistical research field within its historical context and to explore how its gradual …
Bayesian online regression for adaptive direct illumination sampling
Direct illumination calculation is an important component of any physically-based Tenderer
with a substantial impact on the overall performance. We present a novel adaptive solution …
with a substantial impact on the overall performance. We present a novel adaptive solution …
Conditional Bayesian Quadrature
Z Chen, M Naslidnyk, A Gretton, FX Briol - ar** (SLAM)
method based on multi-agent particle swarm optimized particle filter (MAPSOPF) was …
method based on multi-agent particle swarm optimized particle filter (MAPSOPF) was …
Sobolev spaces, kernels and discrepancies over hyperspheres
This work provides theoretical foundations for kernel methods in the hyperspherical context.
Specifically, we characterise the native spaces (reproducing kernel Hilbert spaces) and the …
Specifically, we characterise the native spaces (reproducing kernel Hilbert spaces) and the …
Symmetry exploits for Bayesian cubature methods
Bayesian cubature provides a flexible framework for numerical integration, in which a priori
knowledge on the integrand can be encoded and exploited. This additional flexibility …
knowledge on the integrand can be encoded and exploited. This additional flexibility …
Gaussian process for radiance functions on the sphere
R Marques, C Bouville… - Computer Graphics …, 2022 - Wiley Online Library
Efficient approximation of incident radiance functions from a set of samples is still an open
problem in physically based rendering. Indeed, most of the computing power required to …
problem in physically based rendering. Indeed, most of the computing power required to …