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,
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