Thursday, February 9, 2006
12.00 noon – 1.00 pm
Intel Seminar (CIC Suite 410)
EVENTS PAGE: http://www.intel-research.net/pittsburgh/events.htm
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.
Contact Kim Kaan, 412-605-1203,
or visit http://www.intel-research.net.
SDI Home: http://www.pdl.cmu.edu/SDI/