Spatiotemporal Stochastic Processes and their Prediction
Cosma Shalizi, Assistant Professor in Statistics, CMU


This talk will continue the over-view of stochastic processes, moving from those which just evolve in time to ones which evolve in time and space, where "space" can be a regular lattice, Euclidean space, a graph, etc. Adding space creates lots of interesting possibilities, which I'll illustrate with "cellular automata" models of physical and biological self-organization. After the challenges this setting raises for statistical learning have had a chance to sink in, I'll describe an approach to discovering efficient "local predictors", and using them to automatically identify interesting coherent structures in spatio-temporal data.


Venue, Date, and Time

Venue: NSH 1507

Date: Monday, November 12

Time: 12:00 noon