\(\renewcommand{\var}{\operatorname{Var}} \renewcommand{\dd}{\mathrm{d}} \renewcommand{\pd}{\partial} \renewcommand{\bb}[1]{\mathbb{#1}} \renewcommand{\bf}[1]{\mathbf{#1}} \renewcommand{\vv}[1]{\boldsymbol{#1}} \renewcommand{\mm}[1]{\mathrm{#1}} \renewcommand{\cc}[1]{\mathcal{#1}} \renewcommand{\oo}[1]{\operatorname{#1}} \renewcommand{\gvn}{\mid} \renewcommand{\II}{\mathbb{I}}\)

Stochastic processes indexed by time whose state is a discrete (possibly only countable) measure. Popular in, for example, mathematical models of alleles in biological evolution.

Population genetics keywords, in approximate order of generality and chronology: Fisher-Wright diffusion, Moran process, Viot-Fleming process. Obviously there are many other processes matching this broad description.

For one example, any element-wise-positive vector-valued stochastic process can be made into a discrete-measure-valued process by normalising the state vector to sum to 1. For another, count time series are realisations of the measures of such as process of these. Chinese-restaurant processes as processes in time presumably fit here, although AFAICT the use of these processes in the literature is usually not the time-evolving construction, but rather the infinite-time limit of such a process, which is confusing nomenclature.

If the process does not take values in discrete measures, that would be a different notebook, which does not at the moment exist; For now, I note that state filters induce such processes, although in an inference setting rather than a purely probabilistic one. The interacting particle systems are important in that context, too. Also, for example, stochastic differential equations are also measure-valued stochastic processes – once again, usually not over discrete measures.

The Dirichlet process, despite its name is not usually sampled as a time process and in any case has boring dynamics. However, it does I believe have time-indexed exnsions (Griffin and Steel 2006; Dunson and Park 2008)