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A distributed hypothesis testing problem is considered, where the goal is to declare the distribution of two random variables, based on their observations. Defining two error events, the error exponent of Type II is studied under a fixed constraint over the error of type I. A novel approach is presented, based on random binning. The benefits of this approach are demonstrated through an example, compared...
Detection of the existence of data streams drawn from outlying distributions among data streams drawn from a typical distribution is investigated. It is assumed that the typical distribution is known and the outlying distribution is unknown. The generalized likelihood ratio test (GLRT) for this problem is constructed. With knowledge of the Kullback-Liebler divergence between the outlier and typical...
We study the problem of testing between a sparse signal in noise, modeled as a mixture distribution, versus pure noise, with finite alphabet observations. We study the consistency and adaptivity of the tests as the mixture proportion tends to zero with number of observations. The finite alphabet assumption allows for application to inherently categorical data, where no useful ordering relationship...
In this paper we develop statistical detection theory for graph signals. In particular, given two graphs, namely, a background graph that represents an usual activity and an alternative graph that represents some unusual activity, we are interested in answering the following question: To which of the two graphs does the observed graph signal fit the best? To begin with, we assume both the graphs are...
We consider the problem of inferring time-varying Granger causal interactions among multiple simultaneously recorded spike trains from a neuronal ensemble. We present a dynamic Granger causality measure with sparsity and adaptivity features for point process observations, and estimate it recursively. We develop a statistical inference framework based on asymptotic analysis of deviance, and perform...
We investigate the task of model selection for high-dimensional data. For this purpose, we propose an extension to the Bayesian information criterion. Our information criterion is asymptotically consistent either as the number of measurements tends to infinity or as the variance of noise decreases to zero. The numerical results provided support our claim. Additionally, we highlight the link between...
We consider the problem of keyless message authentication over noisy channels in the presence of an active adversary. Different from the existing models, in our model, the legitimate users do not have any pre-shared key for authentication. Instead, we use the noisy channel connecting the legitimate users for authentication. The main idea is to utilize the noisy channel connecting the legitimate users...
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