Gaussian Belief With Dynamic Data and in Dynamic Network

Message-passing algorithms have over the last two decades turned out to be an important paradigm in fields as distant as iterative decoding, image processing and AI. It has been realized that systems where the message-passing algorithms are effective can often be assimilated to disordered systems in statistical physics, and that the message-passing algorithms themselves are closely related to the Bethe approximation. Most applications pursued concern inference in static models; how to do this effectively (if approximately), and when these methods work.