A symmetric version of the Neyman-Pearson test is developed for discriminating between sets of hypotheses and is extended to encompass a new formulation of the problem of parameter estimation based on finite data sets. Such problems can arise in distributed sensing and localization problems in sensor networks, where sensor data must be compressed to account for communication constraints. In thi...

This paper proposes a restoration scheme for noisy images generated by coherent imaging systems (e.g., synthetic aperture radar, synthetic aperture sonar, ultrasound imaging, and laser imaging). The approach is Bayesian: the observed image intensity is assumed to be a random variable with gamma density; the image to be restored (mean amplitude) is modeled by a compound Gauss-Markov random eld w...

Abstract—Optimal distributed fusion assuming that sensor decision rules are given is considered. A general and computationally tractable optimal fusion rule is presented, which relies only on the joint conditional probability densities of all sensor observations and all local decision rules. It is valid for general decision systems with any sensor observations and sensor decision rules, regardl...

Neyman-Pearson or frequentist inference and Bayes inference are most clearly differentiated by their approaches to point null hypothesis testing. With very large samples, the frequentist and Bayesian conclusions from a classical test of significance for a point null hypothesis can be contradictory, with a small frequentist P -value casting serious doubt on the null hypothesis, but a large Bayes...

In this paper, the application of neural networks for approximating the Neyman-Pearson detector is considered. We propose a strategy to identify the training parameters that can be controlled for reducing the effect of approximation errors over the performance of the neural network based detector. The function approximated by a neural network trained using the mean squared-error criterion is de...

Our goal is automatic recognition of basic human actions, such as stand, sit and wave hands, to aid in natural communication between a human and a computer. Human actions are inferred from human body joint motions, but such data has high dimensionality and large spatial and temporal variations may occur in executing the same action. We present a learning-based approach for the representation an...

Despite the widespread use of key concepts of the Neyman–Pearson (N–P) statistical paradigm—type I and II errors, significance levels, power, confidence levels—they have been the subject of philosophical controversy and debate for over 60 years. Both current and long-standing problems of N–P tests stem from unclarity and confusion, even among N–P adherents, as to how a test’s (pre-data) error p...

Adding noise to inputs of some suboptimal detectors or estimators can improve their performance under certain conditions. In the literature, noise benefits have been studied for detection and estimation systems separately. In this study, noise benefits are investigated for joint detection and estimation systems. The analysis is performed under the Neyman–Pearson (NP) and Bayesian detection fram...