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Error exponents for composite hypothesis testing of Markov forest distributions

Tan, Vincent Y. F. and Anandkumar, Animashree and Willsky, Alan S. (2010) Error exponents for composite hypothesis testing of Markov forest distributions. In: 2010 IEEE International Symposium on Information Theory. IEEE , Piscataway, NJ, pp. 1613-1617. ISBN 978-1-4244-7890-3. https://resolver.caltech.edu/CaltechAUTHORS:20170922-085233225

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Abstract

The problem of composite binary hypothesis testing of Markov forest (or tree) distributions is considered. The worst-case type-II error exponent is derived under the Neyman-Pearson formulation. Under simple null hypothesis, the error exponent is derived in closed-form and is characterized in terms of the so-called bottleneck edge of the forest distribution. The least favorable distribution for detection is shown to be Markov on the second-best max-weight spanning tree with mutual information edge weights. A necessary and sufficient condition to have positive error exponent is derived.


Item Type:Book Section
Related URLs:
URLURL TypeDescription
https://doi.org/10.1109/ISIT.2010.5513399DOIArticle
http://ieeexplore.ieee.org/document/5513399PublisherArticle
Additional Information:© 2010 IEEE. This work is supported by a AFOSR funded through Grant FA9559-08-1-1080, a MURI funded through ARO Grant W911NF-06-1-0076 and a MURI funded through AFOSR Grant FA9550-06-1-0324. V. Tan is also funded by A*STAR, Singapore.
Funders:
Funding AgencyGrant Number
Air Force Office of Scientific Research (AFOSR)FA9559-08-1-1080
Army Research Office (ARO)W911NF-06-1-0076
Air Force Office of Scientific Research (AFOSR)FA9550-06-1-0324
Agency for Science, Technology and Research (A*STAR)UNSPECIFIED
Subject Keywords:Worst-case error exponent, Markov forests, Least favorable distribution, Neyman-Pearson formulation
Record Number:CaltechAUTHORS:20170922-085233225
Persistent URL:https://resolver.caltech.edu/CaltechAUTHORS:20170922-085233225
Official Citation:V. Y. F. Tan, A. Anandkumar and A. S. Willsky, "Error exponents for composite hypothesis testing of Markov forest distributions," 2010 IEEE International Symposium on Information Theory, Austin, TX, 2010, pp. 1613-1617. doi: 10.1109/ISIT.2010.5513399 URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5513399&isnumber=5513230
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:81735
Collection:CaltechAUTHORS
Deposited By: Tony Diaz
Deposited On:22 Sep 2017 16:06
Last Modified:03 Oct 2019 18:46

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