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Huang, Thomas S. Rajaram, Shyamsundar 2015-09-25T20:09:14Z 2015-09-25T20:09:14Z 10000-01-01 2007 2007 The final part of this dissertation is the development of a new category of graphical models called Poisson networks for modeling structured multivariate structured Poisson processes. Applications for Poisson networks arise in several scenarios, namely, modeling neural spike trains for learning structure of data transmission in the brain, arrival times at nodes for learning the structure of queuing networks, etc. We develop techniques for sampling, inference and structure learning of Poisson networks. Made available in DSpace on 2015-09-25T20:09:14Z (GMT). No. of bitstreams: 2 license.txt: 4848 bytes, checksum: 96035ab3f5e1c23cc7138a224ce498bd (MD5) 3270006.pdf: 3403524 bytes, checksum: 5a7b116d373a51b5702bc1930f7acce0 (MD5) Previous issue date: 2007 Embargo set by: Seth Robbins for item 82299 Lift date: Forever Reason: Restricted to the U of I community idenfinitely during batch ingest of legacy ETDs Restricted to the U of I community idenfinitely during batch ingest of legacy ETDs U of I Only 128 p. Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 2007. http://hdl.handle.net/2142/81017 (MiAaPQ)AAI3270006 eng Engineering, Electronics and Electrical On Modeling Order and Structure With Applications to Computer Vision and Time Series Data text Electrical and Computer Engineering Electrical and Computer Engineering University of Illinois at Urbana-Champaign Dissertation Ph.D.