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Title: Fast variational inference for joint mixed sparse graphical models Authors:  Qingyang Liu - University of Connecticut (United States)
Yuping Zhang - University of Connecticut (United States) [presenting]
Abstract: A statistical learning framework is presented for multiple mixed graphical models via a penalized approximate likelihood estimation. We describe a fast algorithm for variational maximum likelihood inference, which takes advantage of the log-determinant relaxation. We identify a necessary and sufficient condition to discover the connected components in the solution. We then employ a divide-and-conquer approach to the joint structural inference problem for multiple related large sparse networks.