Sentence examples for as normal distribution from inspiring English sources

For the stable distributions, α ranges between 0 and 2. It is characterized as normal distribution for α = 2, while the distribution is Cauchy for α = 1.

Because the carbon number distribution of the precipitated Cn can be adequately regressed as "normal distribution", the standard deviation was used to express the degree of n-alkane polydispersity in precipitated Cn.

However, the research focus began to change as more engineers and applied statisticians found that it was more appropriate to describe the product life distribution of most products as a function belonging to the location-scale distribution, such as normal distribution, Weibull distribution and log-normal distribution, than exponential distribution.

In some NNs, the output signal of the node is transferred to another node by multi-branches with arbitrary time delays, which are random, and its probabilities can often be measured by the statistical methods such as normal distribution, uniform distribution, Poisson distribution, Bernoulli random binary distribution.

Creativity and divergent thinking are sometimes considered as divisions of intelligence, suggesting creativity has a strong genetic basis (and as normal distribution) in population, as intelligence.

The coefficients α0 and α1 were modeled as normal distribution, α ∼ Normal (0, σ2), where σ2 was set to a large constant, and thus both coefficients were treated as non-informative priors.

Using such a linear approximation also fits our function for the periodic process demand from Eq. (1), as normal distributions are invariant against linear transformation.

The algorithm for repairing cost estimation is adopted from the previous research (TOTAL-LCC 2010) where the probability distributions for initial and the extended for service life through repairing are assumed as normal distributions.

In contrast to similar approaches [2], [3] forecast errors of wind and photovoltaic power are not modeled as normal distributions, which does not reflect reality [4], [5], [6], but by kernel density estimation to get more realistic distributions.

We can approximate these as normal distributions clustered around midpoint values.

Before applying parametric tests, we used appropriate tests to assure that assumptions such as normal distributions or homoscedasticity were met.