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Graph-based Estimation of Information Divergence Functions

Abstract Information divergence functions, such as the Kullback-Leibler divergence or the Hellinger distance, play a critical role in statistical signal processing and information theory; however estimating them can be challenge. Most often, parametric assumptions are made about the two distributions to estimate the divergence of interest. In cases where no parametric model fits the data, non-parametric density estimation is used. In statistical signal processing applications, Gaussianity is usually assumed since closed-form expressions for common divergence measures have been derived for this family of distributions. Parametric assumptions are preferred when it is known that the data follows the model, however this is rarely the case in real-word s... (more)
Created Date 2017
Contributor Wisler, Alan (Author) / Berisha, Visar (Advisor) / Spanias, Andreas (Advisor) / Liss, Julie (Committee member) / Bliss, Daniel (Committee member) / Arizona State University (Publisher)
Subject Engineering / Statistics / information thoery / Machine learning / Non-parametric / Performance bounds
Type Doctoral Dissertation
Extent 126 pages
Language English
Reuse Permissions All Rights Reserved
Note Doctoral Dissertation Electrical Engineering 2017
Collaborating Institutions Graduate College / ASU Library
Additional Formats MODS / OAI Dublin Core / RIS

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Description Dissertation/Thesis