Published June 2010
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Error exponents for composite hypothesis testing of Markov forest distributions
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.
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.Attached Files
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Additional details
- Eprint ID
- 81735
- Resolver ID
- CaltechAUTHORS:20170922-085233225
- FA9559-08-1-1080
- Air Force Office of Scientific Research (AFOSR)
- W911NF-06-1-0076
- Army Research Office (ARO)
- FA9550-06-1-0324
- Air Force Office of Scientific Research (AFOSR)
- Agency for Science, Technology and Research (A*STAR)
- Created
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2017-09-22Created from EPrint's datestamp field
- Updated
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2021-11-15Created from EPrint's last_modified field