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


Stochastic processes are collections of interdependent random variables; this talk will be an overview of some of the main concepts, and ways in which they might interest people in machine learning. After a brief mathematical introduction, I focus on stochastic processes whose variables are indexed by time, which are closely related to dynamical systems. The key problem here is understanding the dependence of the variables across time, and the different sorts of long-run behavior to which it can give rise. I will talk about various kinds of dependence structure, especially Markov dependence; how to give Markovian representations of non-Markovian processes; and how to use these Markovian representations for prediction. Finally, I'll close with some recent work on discovering predictive Markovian representations from time series.


Venue, Date, and Time

Venue: NSH 1507

Date: Monday, November 5

Time: 12:00 noon