Computational Science lectures are open to the public and are held at the Gerald D. Fischbach Auditorium at the Simons Foundation headquarters in New York City. Tea is served prior to each lecture.
Belief propagation is an algorithmic method that provides solutions to many complex machine-learning problems.
Belief propagation algorithms have numerous applications in coding theory, vision and many other areas of machine learning. In this talk, Christian Borgs will give an introduction to belief propagation, discuss how the accuracy of belief propagation has been rigorously established, and present recent applications to systems biology. Examples include simple applications to yeast networks, complex applications in the discovery of pathways in cancer genomics, and modifications to distinguish patient-specific pathways from more general disease pathways.