48 citations · 95 across the 6 of their papers we have counts for
6 papers
Probability Measures for Numerical Solutions of Differential Equations
Patrick R. Conrad, Mark Girolami, Simo Särkkä +2
In this paper, we present a formal quantification of epistemic uncertainty induced by numerical solutions of ordinary and partial differential equation models. Numerical solutions…
Batch Nonlinear Continuous-Time Trajectory Estimation as Exactly Sparse Gaussian Process Regression
Sean Anderson, Timothy D. Barfoot, Chi Hay Tong +1
In this paper, we revisit batch state estimation through the lens of Gaussian process (GP) regression. We consider continuous-discrete estimation problems wherein a trajectory is v…
A Bayesian Particle Filtering Method For Brain Source Localisation
Xi Chen, Simo Särkkä, Simon Godsill
In this paper, we explore the multiple source localisation problem in the cerebral cortex using magnetoencephalography (MEG) data. We model neural currents as point-wise dipolar so…
Sparse approximations of fractional Matérn fields
Lassi Roininen, Sari Lasanen, Mikko Orispää +1
We consider a fast approximation method for a solution of a certain stochastic non-local pseudodifferential equation. This equation defines a Matérn class random field. The approxi…
State-Space Inference for Non-Linear Latent Force Models with Application to Satellite Orbit Prediction
Jouni Hartikainen, Mari Seppanen, Simo Sarkka
Latent force models (LFMs) are flexible models that combine mechanistic modelling principles (i.e., physical models) with non-parametric data-driven components. Several key applica…
Sequential Inference for Latent Force Models
Jouni Hartikainen, Simo Sarkka
Latent force models (LFMs) are hybrid models combining mechanistic principles with non-parametric components. In this article, we shall show how LFMs can be equivalently formulated…