4 papers
Pathwise skew-symmetric discretisation for SDEs with superlinear drift
Yuga Iguchi, Samuel Livingstone, Giorgos Vasdekis +1
The skew-symmetric discretisation has recently been proposed as a new robust simulation method for weakly approximating stochastic differential equations (SDEs) with non-globally L…
Dynamic Gaussian Processes and the Vanilla-SPDE Exchange
Rui-Yang Zhang, Lachlan Astfalck, Edward Cripps +2
Gaussian process inference is often limited by cubic computational costs, a challenge that becomes more pronounced in spatio-temporal settings where posterior inference is required…
BALLAST: Bayesian Active Learning with Look-ahead Amendment for Sea-drifter Trajectories under Spatio-Temporal Vector Fields
Rui-Yang Zhang, Lachlan Astfalck, Edward Cripps +2
We introduce a formal active learning methodology for guiding the placement of Lagrangian observers to infer time-dependent vector fields -- a key task in oceanography, marine scie…
Skew-symmetric schemes for stochastic differential equations with non-Lipschitz drift: an unadjusted Barker algorithm
Yuga Iguchi, Samuel Livingstone, Nikolas Nüsken +2
We propose a new simple and explicit numerical scheme for time-homogeneous stochastic differential equations. The scheme is based on sampling increments at each time step from a sk…