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Singer, Andrew C. Kozat, Suleyman Serdar 2015-09-25T20:08:40Z 2015-09-25T20:08:40Z 10000-01-01 2004 2004 Finally, we conclude the thesis by investigating the competitive prediction problem in a probabilistic setting. Here we investigate a particular algorithm and show that this algorithm is universal such that it asymptotically achieves the performance of the best predictor for Gaussian AR sources with unknown order up to some maximal order M. Made available in DSpace on 2015-09-25T20:08:40Z (GMT). No. of bitstreams: 2 license.txt: 4848 bytes, checksum: 96035ab3f5e1c23cc7138a224ce498bd (MD5) 3160907.pdf: 6488916 bytes, checksum: 158ec978fabe71a0f129440fbc13cb9a (MD5) Previous issue date: 2004 Embargo set by: Seth Robbins for item 82174 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 142 p. Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 2004. http://hdl.handle.net/2142/80892 (MiAaPQ)AAI3160907 eng Engineering, Electronics and Electrical Competitive Signal Processing text Electrical Engineering Electrical Engineering University of Illinois at Urbana-Champaign Dissertation Ph.D.