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CORR

2007

Springer

2007

Springer

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.

Related Content

Added |
13 Dec 2010 |

Updated |
13 Dec 2010 |

Type |
Journal |

Year |
2007 |

Where |
CORR |

Authors |
Animashree Anandkumar, Lang Tong, Ananthram Swami |

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