Sentence examples for for sampling implementation from inspiring English sources

In a first phase, the variety in methods that are used for sampling, implementation of the interventions, measures and data analysis were mapped in a systematic literature review using Cochrane guidelines.

In [ 4] we proposed an importance sampling implementation for the Class I reflectance that was based on biased scattering towards the apparent position of the collecting optics and a photon splitting procedure followed by successive biased scatterings towards the apparent position of the collecting optics, whose direction we defined as the unit vector v ^.

The choices for sampling, ED implementation, measurement and analysis, described in the succeeding sections, derive from this model.

These include quantification of metabolites, unequivocal identification of metabolites, provision of longitudinal (time-resolved) dynamic datasets, high throughput (for example, 500 samples a week, with 200 metabolites for each sample), implementation of quality measures [ 18- 21] and standardized reporting [ 22].

In Sec. 3, we show numerical results for the standard Monte Carlo method and for our importance sampling implementations with different parameters.

We propose a novel way for sampled-data implementation (with the zero order hold assumption) of continuous-time controllers for general nonlinear systems.

An example is presented to illustrate the result, and to show the advantages of this direct discrete-time design for sampled-data implementation.

Given a continuous-time controller and a Lyapunov function that shows global asymptotic stability for the closed-loop system, we provide several results for modification of the controller for sampled-data implementation.

Ignoring the transient build up of the FBMC system, the efficiency of the system can be seen as one complex symbol per sample for a critically sampled implementation.

A sample implementation for a mechatronics curriculum at Technische Universität Dresden is given.

Here, we overcome this drawback by a Gibbs sampler implementation for MCMC simulation of the posterior distribution and a GEM algorithm for fast maximum a posteriori point estimation in the low-level C programming language.