Simulating Gaussian processes
March 17, 2022 — March 17, 2022
Gaussian
Hilbert space
kernel tricks
Lévy processes
nonparametric
regression
spatial
stochastic processes
time series
Assumed audience:
ML people
How can I simulate a Gaussian Processes with a given covariance? Handy in GP regression, especially GP functional regression and spatial statistics.
Historical overview in Liu et al. (2019).
1 Krylov subspace methods
The Lanczos trick works here.
TBC
2 Random projection
TBC
3 Langevin dynamics
4 Lattice tricks
On lattices, we can make some computational shortcuts. See GP simulation on lattices.
5 Basis tricks
TBD
6 Simulating from posterior GPs
Probably many tricks, but I know of pathwise GPs.
7 Incoming
8 References
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