Detection error exponent for spatially dependent samples in random networks
The problem of binary hypothesis testing is considered when the measurements are drawn from a Markov random field (MRF) under each hypothesis. Spatial dependence of the measurements is incorporated by explicitly modeling the influence of sensor node locations on the clique potential functions of eac...
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| Format: | Article |
| Language: | en_US |
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Institute of Electrical and Electronics Engineers
2010
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| Online Access: | http://hdl.handle.net/1721.1/54752 https://orcid.org/0000-0003-0149-5888 |
| _version_ | 1811096123960459264 |
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| author | Tong, Lang 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 Tong, Lang Anandkumar, Animashree Willsky, Alan S. |
| author_sort | Tong, Lang |
| collection | MIT |
| description | The problem of binary hypothesis testing is considered when the measurements are drawn from a Markov random field (MRF) under each hypothesis. Spatial dependence of the measurements is incorporated by explicitly modeling the influence of sensor node locations on the clique potential functions of each MRF hypothesis. The nodes are placed i.i.d. in expanding areas with increasing sample size. Asymptotic performance of hypothesis testing is analyzed through the Neyman-Pearson type-II error exponent. The error exponent is expressed as the limit of a functional over dependency edges of the MRF hypotheses for acyclic graphs. Using the law of large numbers for graph functionals, the error exponent is derived. |
| first_indexed | 2024-09-23T16:38:32Z |
| format | Article |
| id | mit-1721.1/54752 |
| institution | Massachusetts Institute of Technology |
| language | en_US |
| last_indexed | 2024-09-23T16:38:32Z |
| publishDate | 2010 |
| publisher | Institute of Electrical and Electronics Engineers |
| record_format | dspace |
| spelling | mit-1721.1/547522022-09-29T20:31:16Z Detection error exponent for spatially dependent samples in random networks Tong, Lang 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 Willsky, Alan S. Anandkumar, Animashree Willsky, Alan S. The problem of binary hypothesis testing is considered when the measurements are drawn from a Markov random field (MRF) under each hypothesis. Spatial dependence of the measurements is incorporated by explicitly modeling the influence of sensor node locations on the clique potential functions of each MRF hypothesis. The nodes are placed i.i.d. in expanding areas with increasing sample size. Asymptotic performance of hypothesis testing is analyzed through the Neyman-Pearson type-II error exponent. The error exponent is expressed as the limit of a functional over dependency edges of the MRF hypotheses for acyclic graphs. Using the law of large numbers for graph functionals, the error exponent is derived. United States. Army Research Laboratory. Communications & Networks Alliance United States. Army Research Office (Grant ARO-W911NF-06-1-0346) United States. Army Research Laboratory. Collaborative Technology Alliances (CTA) Program (Cooperative Agreement DAAD19-01-2-0011) 2010-05-11T15:38:16Z 2010-05-11T15:38:16Z 2009-08 2009-06 Article http://purl.org/eprint/type/ConferencePaper 978-1-4244-4313-0 978-1-4244-4312-3 INSPEC Accession Number: 10842411 http://hdl.handle.net/1721.1/54752 Anandkumar, A., A. Willsky, and Lang Tong. “Detection error exponent for spatially dependent samples in random networks.” Information Theory, 2009. ISIT 2009. IEEE International Symposium on. 2009. 2882-2886. © 2009 Institute of Electrical and Electronics Engineers. https://orcid.org/0000-0003-0149-5888 en_US http://dx.doi.org/10.1109/ISIT.2009.5205358 IEEE International Symposium on Information Theory, 2009. ISIT 2009. 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 |
| spellingShingle | Tong, Lang Anandkumar, Animashree Willsky, Alan S. Detection error exponent for spatially dependent samples in random networks |
| title | Detection error exponent for spatially dependent samples in random networks |
| title_full | Detection error exponent for spatially dependent samples in random networks |
| title_fullStr | Detection error exponent for spatially dependent samples in random networks |
| title_full_unstemmed | Detection error exponent for spatially dependent samples in random networks |
| title_short | Detection error exponent for spatially dependent samples in random networks |
| title_sort | detection error exponent for spatially dependent samples in random networks |
| url | http://hdl.handle.net/1721.1/54752 https://orcid.org/0000-0003-0149-5888 |
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