Computer science colloquia: Jin Tian
| Date/Time: | Thursday, 25 Oct 2012 at 3:40 pm |
|---|---|
| Location: | B29 Atanasoff Hall |
| Cost: | Free |
| Phone: | 515-294-6516 |
| Channel: | College of Liberal Arts and Sciences |
| Categories: | Lectures |
| Actions: | Download iCal/vCal | Email Reminder |
Bayesian networks have become the method of choice for representing and reasoning about uncertainty and causality. A Bayesian network provides a compact representation of a joint probability over a set of random variables, and supports efficient algorithms for answering probabilistic queries. They are being used in a variety of domains such as diagnosis, data mining, pattern recognition, and computational biology. One major challenge in the applications of Bayesian networks
is to learn the model structures from data. In this talk, Jin Tian will introduce the Bayesian model averaging approach to structure learning. He will discuss several different methods and present some recent development.
Jin Tian is an associate professor in the department of computer
science at Iowa State University. He received his Ph.D. in computer
science from UCLA in 2002. His research interests include artificial
intelligence and machine learning currently focusing on probabilistic
reasoning and causal inference in graphical models.