196 citations · 699 across the 80 of their papers we have counts for
7 papers · 1 filter
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…
Combining Particle MCMC with Rao-Blackwellized Monte Carlo Data Association for Parameter Estimation in Multiple Target Tracking
Juho Kokkala, Simo Särkkä
We consider state and parameter estimation in multiple target tracking problems with data association uncertainties and unknown number of targets. We show how the problem can be re…
Gaussian filtering and variational approximations for Bayesian smoothing in continuous-discrete stochastic dynamic systems
Juha Ala-Luhtala, Simo Särkkä, Robert Piché
The Bayesian smoothing equations are generally intractable for systems described by nonlinear stochastic differential equations and discrete-time measurements. Gaussian approximati…
Moment Conditions for Convergence of Particle Filters with Unbounded Importance Weights
Isambi S. Mbalawata, Simo Särkkä
In this paper, we derive moment conditions for particle filter importance weights, which ensure that the particle filter estimates of the expectations of bounded Borel functions co…