Electrical Engineering and Computer Science


AI Seminar

Controlled Predictive State Models for Dynamical Systems with Continuous Observations

Matt Rudary


 
Thursday, March 23, 2006
4:00pm - 5:30pm
3725 Beyster Bldg. (Stained-glass Conference Room)

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Snacks provided.

About the Event

Predictive state representations are an interesting class of models in which the state of the system consists of predictions about the future, in contrast to distributions over latent variables as in many traditional models. I will present the controlled Predictive Linear-Gaussian (PLG) model, a predictive state model for controlled, discrete-time dynamical systems with continuous-valued observations. The PLG has equivalent reprentational power to the Linear Dynamical System / Kalman filter model. I will also outline an algorithm that can be used to obtain consistent estimates of the model's parameters from data, and present experimental results comparing it to the Expectation Maximization algorithm for Linear Dynamical Systems.

Additional Information

Contact: Bob Marinier

Email: rmarinie@umich.edu

Sponsor(s): AI Lab

Open to: Public