Application-specific compression for time delay estimation in sensor networks
Proceedings of the 1st international conference on Embedded networked sensor systems
A framework for MAC protocol misbehavior detection in wireless networks
Proceedings of the 4th ACM workshop on Wireless security
Optimal decision network with distributed representation
Neural Networks
An Analytic Framework for Modeling and Detecting Access Layer Misbehavior in Wireless Networks
ACM Transactions on Information and System Security (TISSEC)
IEEE Transactions on Information Theory
One shot schemes for decentralized quickest change detection
IEEE Transactions on Information Theory
A sequential procedure for simultaneous detection and state estimation of Markov signals
ISIT'09 Proceedings of the 2009 IEEE international conference on Symposium on Information Theory - Volume 1
Initiation and termination of integration in a decision process
Neural Networks
Optimal decision making on the basis of evidence represented in spike trains
Neural Computation
Reward-modulated hebbian learning of decision making
Neural Computation
Generalization of sequential Wald's test for more than two hypotheses
MACMESE'10 Proceedings of the 12th WSEAS international conference on Mathematical and computational methods in science and engineering
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The problem of sequential testing of multiple hypotheses is considered, and two candidate sequential test procedures are studied. Both tests are multihypothesis versions of the binary sequential probability ratio test (SPRT), and are referred to as MSPRTs. The first test is motivated by Bayesian optimality arguments, while the second corresponds to a generalized likelihood ratio test. It is shown that both MSPRTs are asymptotically optimal relative not only to the expected sample size but also to any positive moment of the stopping time distribution, when the error probabilities or, more generally, risks associated with incorrect decisions are small. The results are first derived for the discrete-time case of independent and identically distributed (i.i.d.) observations and simple hypotheses. They are then extended to general, possibly continuous-time, statistical models that may include correlated and nonhomogeneous observation processes. It also demonstrated that the results can be extended to hypothesis testing problems with nuisance parameters, where the composite hypotheses, due to nuisance parameters, can be reduced to simple ones by using the principle of invariance. These results provide a complete generalization of the results given by Veeravalli and Baum (see ibid., vol.41, p.1994-97, 1995), where it was shown that the quasi-Bayesian MSPRT is asymptotically efficient with respect to the expected sample size for i.i.d. observations