# 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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