Sentence examples for distribution at hand from inspiring English sources

The first aspect concerns the existence, convergence, and computational complexity of optimal algorithms tailored (matched) to a particular CES distribution at hand.

The most straightforward way is to take a random sample of K trees directly from the empirical distribution at hand.

With the number of reported cases and the cumulative delay distribution at hand, it is possible to estimate the number of hospitalizations that are still to be reported.

The straightforward convolution of K distributions at hand is an intricate technical problem, and therefore, we suggest an alternative approach.

Hence, we can hypothesize that our distributions at hand are also heavy tailed distributions that will most likely follow a power law model.

This is a result more in line with previous research and the outcome of experiment I, a fact that seems to indicate a bad functioning of (M_{unique}) that makes this method unable to properly capture the underlying noise distribution of the task at hand.

First, the distribution of the data at hand may vary substantially.

We thus view a particular set of conformations as a sample from an underlying distribution and aim to model this distribution based on the sample at hand.

For back-and-forth conversion of the prerun samples and proposals [ 23], we naturally applied the according prior distributions of the models at hand.

Instead, our recovery by refitting resorted to random sampling of the explanatory variables from the target population for biomass estimation, and residuals from a distribution deemed realistic to the case at hand (e.g. a gamma distribution for multiplicative residuals).

Unlike the frequentist approach, in the Bayesian approach any a priori knowledge about the probability distribution function that one assumes might have generated the given data (in the first place) can be taken into account when estimating this distribution function from the data at hand.