Intel Research Seminar

DATE: Thursday, February 9, 2006
TIME: 12.00 noon – 1.00 pm
PLACE: Intel Seminar (CIC Suite 410)

Geoff Gordon

A Tutorial on Monte-Carlo Methods

Monte-Carlo methods are popular ways to approximate the answer to problems like computing integrals, evaluating marginal probabilities, or finding the optimum of a function. They include algorithms like importance sampling and particle filters, as well as Markov-chain Monte-Carlo methods such as Metropolis-Hastings (of which a special case is Gibbs sampling). I will go over examples of how to use each of these algorithms.

Dr. Gordon is a research faculty in the Center for Automated Learning and Discovery at Carnegie Mellon University. He works on multi-robot systems, statistical machine learning, and planning in probabilistic and adversarial domains. His previous appointments include Visiting Professor at the Stanford Computer Science Department and Principal Scientist at Burning Glass Technologies in San Diego. Dr. Gordon received his B.A. in Computer Science from Cornell University in 1991, and his Ph.D. in Computer Science from Carnegie Mellon University in 1999.

For Further Seminar Info:
Contact Kim Kaan, 412-605-1203, or visit

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