imply a theoretical “cross-over ” error rate of one in 131000 when a decision criterion is adopted that would equalize the false accept and false reject error rates. In the typical recognition case, given the mean observed degree of iris code agreement, the decision confidence levels correspond formally

should be addressed to Shelley E. Taylor, University of California, Department of Psychology, 405 Hilgard Avenue, Los Angeles, California 90024-1563. Our healthy individuals find it possible to accept themselves and their own nature without chagrin or complaint.. . . They can accept their own human

that is often required to simulate a desired amount of system time. Presented here is the ''-leap'' method, an approximate procedure that in some circumstances can produce significant gains in simulation speed with acceptable losses in accuracy. Some primitive strategies for control

Motivation: DNA microarrays have recently been used for the purpose of monitoring expression levels of thousands of genes simultaneously and identifying those genes that are differentially expressed. The probability that a false identification (type I error) is committed can increase sharply when

;yes") or new (by responding "no"). Performance on a yesno recognition test is summarized by two measures: the hit rate, the probability that the subject classifies an old item as old, and the false alarm rate, the probability that the subject classifies a new item as old. A given model

system does not demand a very low false acceptance rate. Finally, we show that the matching performance can be improved by combining the decisions of the matchers based on complementary (minutiae-based and filter-based) fingerprint information.

that of state-of the-art methods w.r.t. recognition rate tests, it was equivalent on verification tests. These results indicate that recognition rates can be improved by considering more sophisticated decision rules based on confidence levels.

Motivation: Recent attempts to account for multiple testing in the analysis of microarray data have focused on controlling the false discovery rate (FDR).However, rigorous control of the FDR at a preselected level is often impractical. Consequently, it has been suggested to use the q

Reputation systems can be tricked by the spread of false reputation ratings, be it false accusations or false praise. Simple solutions such as exclusively relying on one's own direct observations have drawbacks, as they do not make use of all the information available. We propose a fully

. The likelihood ratio (LR) test used in the Neyman-Pearson theorem directly maximizes the genuine accept rate (GAR) at any desired false accept rate (FAR). The densities of genuine and impostor match scores needed for the LR test are estimated using finite Gaussian mixture models. We also extend the likelihood