In this short preliminary note I apply the methodology of gametheoretic probability to calculating non-asymptotic confidence intervals for the coefficient of a simple first order scalar autoregressive model. The most distinctive feature of the proposed procedure is that with high probability

Strong Confidence is a new method for setting frequentist limits that enjoys a large number of good properties[1], including that of being free from all those conceptual difficulties that have been of concern in the HEP community in past few years. Probably its most important characteristic

We apply a new prescription in confidence interval estimation, based on the frequentist method of strong Confidence Level (), to make an inference on

of the geographic areas for which data are available. As a conse-quence, comparisons of the degree of geographic concentration across industries can be made with more confidence. Our empiri-cal results provide a strong reaffirmation of the previous wisdom in that we find almost all industries to be somewhat

In typical classification tasks, we seek a function which assigns a label to a single object. Kernel-based approaches, such as support vector machines (SVMs), which maximize the margin of confidence of the classifier, are the method of choice for many such tasks. Their popularity stems both from

of contradicting reasons improved the appropriateness of confidence. Correla-tional analyses of the data of Experiment 1 strongly suggested that the con-fidence depends on the amount and strength of the evidence supporting the answer chosen. e remarkable characteristic of human>ry is its knowledge of its own

We consider tracking as a binary classification problem, where an ensemble of weak classifiers is trained on-line to distinguish between the object and the background. The ensemble of weak classifiers is combined into a strong classifier using AdaBoost. The strong classifier is then used to label

legislated changes into those taken for reasons related to prospective economic conditions and those taken for more exogenous reasons. The behavior of output following these more exogenous changes indicates that tax increases are highly contractionary. The effects are strongly significant, highly robust

of sampling scheme (the placement of sample points on the stimulus axis) strongly affects the reliability of bootstrap confidence intervals, and we make recommendations on how to sample the psychometric function efficiently. Fourth, we show that, under certain circumstances, the (arbitrary) choice

This paper contributes to the conceptual and empirical understanding of organizational trust. It confirms the importance of “contextual confidence ” in institutions for building trust. Moreover, it extends models of trust production to include the effects of purposive action by the truster over