Title :
Detection error exponent for spatially dependent samples in random networks
Author :
Anandkumar, Animashree ; Tong, Lang ; Willsky, Alan
Author_Institution :
ECE Dept., Cornell Univ., Ithaca, NY, USA
fDate :
June 28 2009-July 3 2009
Abstract :
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.
Keywords :
Markov processes; distributed sensors; graph theory; signal detection; Markov random field; acyclic graphs; binary hypothesis testing; clique potential functions; detection error exponent; hypothesis testing asymptotic performance; random networks; sensor node locations; Gaussian distribution; Gaussian processes; Guidelines; Hidden Markov models; Lattices; Markov random fields; Performance analysis; Random variables; Stochastic processes; Testing; Error exponent; Markov random field; law of large numbers for graph functionals; random graphs;
Conference_Titel :
Information Theory, 2009. ISIT 2009. IEEE International Symposium on
Conference_Location :
Seoul
Print_ISBN :
978-1-4244-4312-3
Electronic_ISBN :
978-1-4244-4313-0
DOI :
10.1109/ISIT.2009.5205358
