Speaker: Scott McKinley (Tulane University)
Title: Stochastic fountain dynamics and associated challenges for inference
Abstract: In the last couple of years, I have noticed an emerging theme in my work. Across multiple biological systems, colleagues and I have articulated models that involve particles that (1) emerge at random times from a fixed source-location distribution; (2) move throughout a local environment randomly (either diffusing, or switching between deterministic states); and (3) are removed from the system due to state-switching or escape from some predefined region.
We have been tentatively calling these systems “stochastic fountains,” and have been studying what these systems look like when you only have access to partial information. For example, what if you only have a snapshot of particles at one instant in time? Or, what happens if you can only observe particles at the moment they leave the domain? The associated inference problems arise naturally in mathematical biology applications, and are a fascinating example of how statistical (Fisher) information can be used to assess the quality of data in partially observed systems.