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Efficiently Learning Probabilistic Graphical Models

Portland State University FAB, Room 86-09
1900 SW Fourth Avenue
Portland, Oregon 97201, US (map)

Access Notes

Building is at 4th and College. Room 86-01 is in the basement, take the elevator or stairs down to basement and follow the signs.




Probabilistic graphical models are used to represent uncertainty in many domains, such as error-correcting codes, computational biology, sensor networks and medical diagnosis. This talk will discuss two approaches to the problem of learning graphical models from data, focusing on computational challenges. The first is marginalization-based learning, where parameters are fit in the context of a specific approximate inference algorithm. This will include results on image processing and computer vision problems. The second is recent work on Markov chain Monte Carlo based learning, inspired by a computational biology project.