PSU Mark
Eberly College of Science Mathematics Department

Meeting Details

For more information about this meeting, contact Kris Jenssen, Yuxi Zheng.

Title:Using Markov Chains to Calibrate High Dimensional Models
Seminar:CCMA Luncheon Seminar
Speaker:John C. Liechty, Smeal College of Business, Penn State University
Abstract:
The ability to construct a stationary or target distribution, that is consistent with a particular statistical model (predictive model + error) of interest and allows for the possibility of calibrating this model by creating a discrete-time, general state-space Markov chain over the space of possible parameter values. The Hastings-Metropolis algorithm, which originally came from Statistical Physics, offers a way of constructing a Markov Chain that has the desired 'target' distribution as its unique stationary distribution - a calibration effort which is known as Markov Chain Monte Carlo. I will motivate the theory by reviewing some relevant discrete-time Markov Chain theory and then give a few illustrative examples from the Bayesian Statistical literature.

Room Reservation Information

Room Number:MB114
Date:02 / 26 / 2010
Time:12:00pm - 01:30pm