Convolutional subordinator processes
March 8, 2021 — December 1, 2021
functional analysis
kernel tricks
machine learning
PDEs
physics
point processes
regression
spatial
statistics
stochastic processes
time series
uncertainty
Defining stochastic processes by convolution of noise with smoothing kernels, where the driving noise is a Lévy subordinator.
Why would we want this? One reason is that this gives us a way to create nonparametric distributions over measures.
A related but distinct technique is that we can create interesting Generalized Gamma convolution,
1 References
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