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946
Data Selection in Binary Hypothesis Testing
, 2003
"... Traditionally, statistical signal processing algorithms are developed from probabilistic models for data. The design of the algorithms and their ultimate perfomance depend upon these assumed models. In certain situations, collecting or processing all available measurements may be inefficient or proh ..."
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the probabilistic description of distinct data subsets can vary significantly. An algorithm designed for the probabilistic description of a poorly chosen data subset can lose much of the potential performance available to a wellchosen subset. This thesis considers algorithms for data selection combined with binary
THE DEPARTURE OF η CARINAE FROM AXISYMMETRY AND THE BINARY HYPOTHESIS
, 2001
"... I argue that the large scale departure from axisymmetry of the η Carinae nebula can be explained by the binary stars model of η Carinae. The companion diverts the wind blown by the primary star, by accreting from the wind and possibly by blowing its own collimated fast wind (CFW). The effect of thes ..."
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I argue that the large scale departure from axisymmetry of the η Carinae nebula can be explained by the binary stars model of η Carinae. The companion diverts the wind blown by the primary star, by accreting from the wind and possibly by blowing its own collimated fast wind (CFW). The effect
A Nonparametric Training Algorithm for Decentralized Binary Hypothesis Testing Networks
 Proceedings of 1993 American Control Conference
, 1993
"... We derive a nonparametric training algorithm which asymptotically achieves the minimum possible error rate, over the set of linear classifiers, for decentralized binary hypothesis testing (detection) networks. The training procedure is nonparametric in the sense that it does not require the function ..."
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Cited by 1 (0 self)
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We derive a nonparametric training algorithm which asymptotically achieves the minimum possible error rate, over the set of linear classifiers, for decentralized binary hypothesis testing (detection) networks. The training procedure is nonparametric in the sense that it does not require
Optimal Distributed Binary Hypothesis testing with Independent Identical Sensors
 Department of Computer Engineering and Informatics, University of Patras
, 2000
"... We consider the problem of distributed binary hypothesis testing with independent identical sensors. It is well known that for this problem the optimal sensor rules are a likelihood ratio threshold tests and the optimal fusion rule is a KoutofN rule [1]. Under the Bayesian criterion, we show that ..."
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Cited by 3 (1 self)
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We consider the problem of distributed binary hypothesis testing with independent identical sensors. It is well known that for this problem the optimal sensor rules are a likelihood ratio threshold tests and the optimal fusion rule is a KoutofN rule [1]. Under the Bayesian criterion, we show
1Distributed Sequential Detection for Gaussian Binary Hypothesis Testing
"... This paper studies the problem of sequential Gaussian binary hypothesis testing in a distributed multiagent network. A sequential probability ratio test (SPRT) type algorithm in a distributed framework of the consensus+innovations form is proposed, in which the agents update their decision statist ..."
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This paper studies the problem of sequential Gaussian binary hypothesis testing in a distributed multiagent network. A sequential probability ratio test (SPRT) type algorithm in a distributed framework of the consensus+innovations form is proposed, in which the agents update their decision
Metabolic stability and epigenesis in randomly connected nets
 Journal of Theoretical Biology
, 1969
"... “The world is either the effect of cause or chance. If the latter, it is a world for all that, that is to say, it is a regular and beautiful structure.” Marcus Aurelius Protoorganisms probably were randomly aggregated nets of chemical reactions. The hypothesis that contemporary organisms are also r ..."
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Cited by 657 (5 self)
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“The world is either the effect of cause or chance. If the latter, it is a world for all that, that is to say, it is a regular and beautiful structure.” Marcus Aurelius Protoorganisms probably were randomly aggregated nets of chemical reactions. The hypothesis that contemporary organisms are also
Loopy belief propagation for approximate inference: An empirical study. In:
 Proceedings of Uncertainty in AI,
, 1999
"... Abstract Recently, researchers have demonstrated that "loopy belief propagation" the use of Pearl's polytree algorithm in a Bayesian network with loops can perform well in the context of errorcorrecting codes. The most dramatic instance of this is the near Shannonlimit performanc ..."
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Cited by 676 (15 self)
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the convergence the more exact the approximation. • If the hidden nodes are binary, then thresholding the loopy beliefs is guaranteed to give the most probable assignment, even though the numerical value of the beliefs may be incorrect. This result only holds for nodes in the loop. In the maxproduct (or "
Sparsity from binary hypothesis testing and application to nonparametric estimation
 in European Signal Processing Conference, EUSIPCO’08
, 2008
"... This paper presents and discusses an alternative notion of sparsity. This notion derives from a theoretical result in binary hypothesis testing and slightly differs from the standard notion of sparsity introduced by Donoho and Johnstone. As an application of this alternative notion of sparsity and ..."
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Cited by 4 (3 self)
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This paper presents and discusses an alternative notion of sparsity. This notion derives from a theoretical result in binary hypothesis testing and slightly differs from the standard notion of sparsity introduced by Donoho and Johnstone. As an application of this alternative notion of sparsity
A Generalized Sequential Sign Detector for Binary Hypothesis Testing
, 1998
"... It is known that for fixed error probabilities sequential signal detection based on the sequential probability ratio test (SPRT) is optimum in terms of the average number of signal samples for detection. But, often suboptimal detectors like the sequential sign detector are preferred over the optima ..."
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Cited by 5 (4 self)
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, a generalized sequential sign detector for detecting binary signals in stationary, first order Markov dependent noise is studied. Under iid assumptions, this reduces to the usual sequential sign detector. The optimal decision thresholds and the average sample number for the test to terminate
Results 1  10
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