This group is interested in a variety of problems in statistical communication theory. Our current research is concerned primarily with: measurement of correlation functions, location of noise sources in space by correlation methods, statistical behavior of coupled oscillators, nonlinear feedback systems, stochastic approximation methods in the analysis of nonlinear systems, measurement of the ...

By combining information theory, statistical decision theory, and maximum entropy to address the decision fusion problems, a statistical decision fusion theory is obtained. The theory explains why decision fusion is so difficult and why the performance of decision fusion systems does not always meet expectations. The theory suggests how statistical decision systems such as the conceptual "Famil...

1 Statistical Theory of Quantization Bernard Widrow, Life Fellow, IEEE , Istv an Koll ar, Senoir Member, IEEE , and Ming-Chang Liu Abstract |The e ect of uniform quantization can often be modeled by an additive noise that is uniformly distributed, uncorrelated with the input signal, and has a white spectrum. This paper surveys the theory behind this model, and discusses the conditions of its va...

Hajek's [17] convolution theorem was a major advance in understanding the classical information inequality. This re-examination of the convolution theorem discusses historical background to asymptotic estimation theory; the role of superefficiency in current estimation practice; the link between convergence of bootstrap distributions and convolution structure; and a dimensional asymptotics view...