Abstract:

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.