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 well-chosen subset. This thesis considers algorithms for data selection combined with binary

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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

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

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 K-out-of-N rule [1]. Under the Bayesian criterion, we show

This paper studies the problem of sequential Gaussian binary hypothesis testing in a distributed multi-agent net-work. 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

“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 Proto-organisms probably were randomly aggregated nets of chemical reactions. The hypothesis that contemporary organisms are also

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 max-product (or "

This paper presents and discusses an alternative notion of sparsity. This notion derives from a theoretical result in bi-nary 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 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