Error exponents for composite hypothesis testing of Markov forest distributions
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 o...
| Main Authors: | , , |
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| Other Authors: | |
| Format: | Article |
| Language: | en_US |
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Institute of Electrical and Electronics Engineers (IEEE)
2012
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| Online Access: | http://hdl.handle.net/1721.1/73578 https://orcid.org/0000-0003-0149-5888 |
| _version_ | 1811072753661378560 |
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| author | Tan, Vincent Yan Fu Anandkumar, Animashree Willsky, Alan S. |
| author2 | Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science |
| author_facet | Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science Tan, Vincent Yan Fu Anandkumar, Animashree Willsky, Alan S. |
| author_sort | Tan, Vincent Yan Fu |
| collection | MIT |
| description | 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. |
| first_indexed | 2024-09-23T09:11:48Z |
| format | Article |
| id | mit-1721.1/73578 |
| institution | Massachusetts Institute of Technology |
| language | en_US |
| last_indexed | 2024-09-23T09:11:48Z |
| publishDate | 2012 |
| publisher | Institute of Electrical and Electronics Engineers (IEEE) |
| record_format | dspace |
| spelling | mit-1721.1/735782022-09-26T11:01:28Z Error exponents for composite hypothesis testing of Markov forest distributions Tan, Vincent Yan Fu Anandkumar, Animashree Willsky, Alan S. Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology. Laboratory for Information and Decision Systems Tan, Vincent Yan Fu Anandkumar, Animashree Willsky, Alan S. 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. 2012-10-03T19:16:37Z 2012-10-03T19:16:37Z 2010-07 2010-06 Article http://purl.org/eprint/type/ConferencePaper 978-1-4244-7891-0 978-1-4244-7890-3 http://hdl.handle.net/1721.1/73578 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 https://orcid.org/0000-0003-0149-5888 en_US http://dx.doi.org/10.1109/ISIT.2010.5513399 Proceedings of the IEEE International Symposium on Information Theory Proceedings (ISIT), 2010 Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use. application/pdf Institute of Electrical and Electronics Engineers (IEEE) IEEE |
| spellingShingle | Tan, Vincent Yan Fu Anandkumar, Animashree Willsky, Alan S. Error exponents for composite hypothesis testing of Markov forest distributions |
| title | Error exponents for composite hypothesis testing of Markov forest distributions |
| title_full | Error exponents for composite hypothesis testing of Markov forest distributions |
| title_fullStr | Error exponents for composite hypothesis testing of Markov forest distributions |
| title_full_unstemmed | Error exponents for composite hypothesis testing of Markov forest distributions |
| title_short | Error exponents for composite hypothesis testing of Markov forest distributions |
| title_sort | error exponents for composite hypothesis testing of markov forest distributions |
| url | http://hdl.handle.net/1721.1/73578 https://orcid.org/0000-0003-0149-5888 |
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