On Modeling Order and Structure With Applications to Computer Vision and Time Series Data
Rajaram, Shyamsundar
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Permalink
https://hdl.handle.net/2142/81017
Description
Title
On Modeling Order and Structure With Applications to Computer Vision and Time Series Data
Author(s)
Rajaram, Shyamsundar
Issue Date
2007
Doctoral Committee Chair(s)
Huang, Thomas S.
Department of Study
Electrical and Computer Engineering
Discipline
Electrical and Computer Engineering
Degree Granting Institution
University of Illinois at Urbana-Champaign
Degree Name
Ph.D.
Degree Level
Dissertation
Keyword(s)
Engineering, Electronics and Electrical
Language
eng
Abstract
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.
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