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arXiv · 0706.1588

Detection of Gauss-Markov Random Fields with Nearest-Neighbor Dependency

Animashree Anandkumar·Lang Tong·Ananthram Swami
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Abstract

The problem of hypothesis testing against independence for a Gauss-Markov random field (GMRF) is analyzed. Assuming an acyclic dependency graph, an expression for the log-likelihood ratio of detection is derived. Assuming random placement of nodes over a large region according to the Poisson or uniform distribution and nearest-neighbor dependency graph, the error exponent of the Neyman-Pearson detector is derived using large-deviations theory. The error exponent is expressed as a dependency-graph functional and the limit is evaluated through a special law of large numbers for stabilizing graph functionals. The exponent is analyzed for different values of the variance ratio and correlation. It is found that a more correlated GMRF has a higher exponent at low values of the variance ratio whereas the situation is reversed at high values of the variance ratio.

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Animashree Anandkumar, Lang Tong, Ananthram Swami. 2007-06-11. Detection of Gauss-Markov Random Fields with Nearest-Neighbor Dependency. https://doi.org/10.1109/tit.2008.2009855

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