Subordinator priors and completely random measures

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A subordinator is an a.s. non-decreasing Lévy process. These can be used a random measures, which means they can be used as priors, which means they are handy for Bayesian nonparametrics. These priors include as a special case Dirichlet process priors in a way I will make precise maybe one day.

A rule of thumb is that while Gaussian process regression is good for giving you curves and more generally, fields, a subordinator prior gives you measures. They are always kinda lumpy measures. It is easy to conserve mass with these things.


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