Sentence examples for because the probability distribution from inspiring English sources

There are two kinds of pattern identification unit: One uses a parametric method for which the probability distribution of data is known in advance and another uses a nonparametric method which requires collected data because the probability distribution of data is unknown.

The average concentrations generally exceed the median concentrations, which is not surprising because the probability distribution of concentrations usually is skewed to the right.

Because the probability distribution of streamflow changes from day to day, the 5th and 95th smoothed estimates of the percentiles of streamflow on each day are plotted as black lines.

This is true for both estimated values, because streamflow changes from day to day, and for FN values, because the probability distribution of streamflow changes from day to day (for instance, streamflow is often high on days in the spring, when snowmelt and storm events increase flow in these rivers).

It is possible to use this because the probability distribution of the null hypothesis, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document} $$Ps_11, s_2,..., s_n|r = \bar R$$\end{documentt} ), is known.

The empirical probabilities P ˆ (e i ) are known as edge intensities or arc strengths, and can be interpreted as the degree of confidence that e i is present in the network structure G 0 describing the true dependence structure of X . 1 However, they are difficult to evaluate, because the probability distribution of the networks G b in the space of the network structures is unknown.

Because the probability distributions of the intensity scores were not always normal, we used the median values of the subjects' ratings rather than their means.

Insofar as a distribution f xi) follows this kind of pattern, one can interpret the mean of f as a rough measure of location of the bulk of the probability distribution, because in the defining sum the values xi associated with large values of f xi) more or less define the centre of the distribution.

However, we do not need this prior, because the lower bound on the probability distribution masks it.

The re-weighting method is for local sensitivity analysis, because the underlying theory requires the probability distribution of the estimator of the parameter of interest under MAR and MNAR to share the same support (albeit they have different means).

Because of this, we may reduce the probability distribution to a two-dimensional subspace of configuration space and still access sufficient information to understand the behavior of the model.