Single-Sampling Strategy vs. Multi-Sampling Strategy for Blood Cultures in Sepsis: A Prospective Non-inferiority Study

Developing a Sampling Strategy

Quality Sampling Strategy

Water quality monitoring programs have expanded in an effort to quantify loadings to streams and lakes from various watershed activities and managements. At the core of monitoring programs are strategies or schemes that determine when and how samples are taken for estimating stream loadings. Quantification of the differences between these schemes has not been adequately documented. An analytic ...

Annex B: Sampling strategy

The basic idea of the proposed strategy is to use multiple chains sampled using the Metropolis Hasting algorithm (MH). The transition kernel we used for each chain is an isotropic Gaussian with small variance combined with a (randomly chosen) single parameter Gaussian proposal with same variance. The variance of the proposal kernel was determined based on early simulations in order to obtain an...

A Selective Sampling Strategy for Label Ranking

Wepropose a novel active learning strategy based on the compression framework of [9] for label ranking functions which, given an input instance, predict a total order over a predefined set of alternatives. Our approach is theoretically motivated by an extension to ranking and active learning of Kääriäinen’s generalization bounds using unlabeled data [7], initially developed in the context of cl...

A Statistical Sampling Strategy for Iris Recognition

We present a new approach for iris recognition based on a random sampling strategy. Iris recognition is a method to identify individuals, based on the analysis of the eye iris. This technique has received a great deal of attention lately, mainly due to iris unique characterics: highly randomized appearance and impossibility to alter its features. A typical iris recognition system is composed of...