11 citations · 40 across the 18 of their papers we have counts for
6 papers · 1 filter
ProbNum: Probabilistic Numerics in Python
Jonathan Wenger, Nicholas Krämer, Marvin Pförtner +9
Probabilistic numerical methods (PNMs) solve numerical problems via probabilistic inference. They have been developed for linear algebra, optimization, integration and differential…
Scale estimation and rate-unbiasedness for Gaussian processes under smoothness misspecification
Toni Karvonen, François Bachoc
Gaussian process regression is used throughout statistics and machine learning for prediction and uncertainty quantification. A Gaussian process is specified by its mean and covari…
Black Box Probabilistic Numerics
Onur Teymur, Christopher N. Foley, Philip G. Breen +2
Probabilistic numerics casts numerical tasks, such the numerical solution of differential equations, as inference problems to be solved. One approach is to model the unknown quanti…
Small Sample Spaces for Gaussian Processes
Toni Karvonen
It is known that the membership in a given reproducing kernel Hilbert space (RKHS) of the samples of a Gaussian process is controlled by a certain nuclear dominance condition.…
Bumps and polynomials in RKHSs of translation-invariant kernels
Toni Karvonen
We use the uncertainty principle of harmonic analysis and the Beurling-Malliavin theorem to describe when a reproducing kernel Hilbert space of a translation-invariant positive-def…
A probabilistic Taylor expansion with Gaussian processes
Toni Karvonen, Jon Cockayne, Filip Tronarp +1
We study a class of Gaussian processes for which the posterior mean, for a particular choice of data, replicates a truncated Taylor expansion of any order. The data consist of deri…