Sentence examples for data can be approximated from inspiring English sources

This effectiveness depends on how precisely the raw data can be approximated and how precisely these approximations can be compared.

Considering the retail problem with stochastic variables, the probability distribution of uncertain data can be approximated by a collection of plausible sets of input data with associated probabilities of occurrence.

For example, alphabet size represents the discretized ranges of values that our raw data can be approximated into while the compression ratio, which is the ratio of the length of the input signal over that of the output signal, determines how much we would like to approximate the data by.

This data can be approximated with a sigmoid function: f(x)=frac{x}{sqrt[a]{b+{x}^2}}, (1 where ( mathrm{f}left(mathrm{x}right) ) is the share of LNG and x is a year in the forecasts used, and a and b determine the shape of the curve.

Since it has been previously shown that SAGE data can be approximated by a Poisson distribution [12], we used Poisson-based rules for our simulations (see methods).

The main assumption is that the incomplete empirical probability distribution (determined by occurrence data) can be approximated with a probability distribution of maximum entropy (the Maxent distribution) subject to certain environmental constraints, and that this distribution approximates the potential geographic distribution of the group of interest [56].

At CTD, data currency can be approximated by establishing the 'age' of data, reflected by the publication year of the article from whence the information was extracted.

The data set can be approximated by an Arrhenius-type relation: D=4.7×10−6 exp(–4560/T) m2/s (T in K), which yields values within a factor of 2 of, but slightly lower than, diffusivities estimated by application of the Stokes-Einstein relation.

In practice, this is accomplished by decomposing the unlabeled data matrix into a matrix of basis vectors representing those "building blocks" and matrix of combination coefficients such that each data sample can be approximated by a linear combination of the basis vectors.

From the reflectance data, optical absorbance can be approximated by the Kubelka-Munk function, as given by Equation 4: (4).

assumption on noise and data, p(φ|h) can be approximated as a complex normal distribution with mean μ = [ μ 1 T, …, μ s ′ T ] T and covariance R φ = diag[R1,..., R s ' ].