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Found 7 papers, 2 papers with code
Scalable Data Assimilation with Message Passing
Data assimilation is a core component of numerical weather prediction systems.
Iterated INLA for State and Parameter Estimation in Nonlinear Dynamical Systems
Data assimilation (DA) methods use priors arising from differential equations to robustly interpolate and extrapolate data.
Gaussian Processes on Cellular Complexes
In this paper, we go beyond this dyadic setting and consider polyadic relations that include interactions between vertices, edges and one of their generalisations, known as cells.
Actually Sparse Variational Gaussian Processes
For large datasets, sparse GPs reduce these demands by conditioning on a small set of inducing variables designed to summarise the data.
Short-term Prediction and Filtering of Solar Power Using State-Space Gaussian Processes
Short-term forecasting of solar photovoltaic energy (PV) production is important for powerplant management.
Vector-valued Gaussian Processes on Riemannian Manifolds via Gauge Independent Projected Kernels
Gaussian processes are machine learning models capable of learning unknown functions in a way that represents uncertainty, thereby facilitating construction of optimal decision-making systems.
A Unifying and Canonical Description of Measure-Preserving Diffusions
A complete recipe of measure-preserving diffusions in Euclidean space was recently derived unifying several MCMC algorithms into a single framework.
