Stochastic Processes, Continuous Model Theory, and Saturation
Abstract: This talk will present a way to use continuous model theory to study of random variables and stochastic processes.
In the literature, nonstandard analysis has been successfully in that area, often leading to new existence results that require saturated probability spaces. This is an application of first order model theory.
Here we pave the way to apply continuous model theory instead. The key is to construct saturated models in which important properties of stochastic processes can be naturally expressed by continuous formulas.