Error exponents for composite hypothesis testing of Markov forest distributions

Author(s)
Tan, Vincent Yan Fu; Anandkumar, Animashree; Willsky, Alan S.
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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.
Date issued
2010-07
URI
http://hdl.handle.net/1721.1/73578
Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science; Massachusetts Institute of Technology. Laboratory for Information and Decision Systems
Journal
Proceedings of the IEEE International Symposium on Information Theory Proceedings (ISIT), 2010
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Citation
Tan, Vincent Y. F., Animashree Anandkumar, and Alan S. Willsky. “Error Exponents for Composite Hypothesis Testing of Markov Forest Distributions.” IEEE International Symposium on Information Theory Proceedings (ISIT), 2010. 1613–1617. © Copyright 2010 IEEE
Version: Final published version
ISBN
978-1-4244-7891-0
978-1-4244-7890-3
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