The main strengths of this national study is the use of representative data of the German general population obtained by generating a random sample by applying random-digit dialing of both landlines and mobile phones and weighing the sample to reflect the distribution of the adult population in Germany.
First, it includes representative data of the German adult population obtained by generating a random sample.
The algorithm works by generating a random sample, of size N g, of the ensemble of networks G ens consistent with the data.
This is achieved by generating a pseudo-sample based on the observed sample population.
IRN is produced by generating a random noise sample, delaying it, and adding it back to the original.
We do so by generating a distribution of samples of g consistent with the data and take the mean value of g as the best estimate and its standard deviation as the error in this estimate.
To avoid this computational burden, the data-dependent expectation can be approximated by generating a finite number of samples from the joint distribution (p({mathbf{u}},{mathbf{h}} )) using the Markov Chain Monte Carlo (MCMC) [24] technique.
SWIFT robustness was evaluated by generating a cluster template from one sample, then assigning cells to this template from three experimental replicate samples from the same subject, or from different subjects analyzed under the same conditions on the same day.
The algorithm proceeds by iteratively performing a number of steps: Generate a sample by sampling from a proposal distribution based on the current state Compute the likelihood of the data and calculate, where refers to the prior density function.
The Metropolis–Hastings algorithm proceeds as follows: Generate a sample by sampling from a proposal distribution based on the current state.
