Sentence examples for distribution unbiased from inspiring English sources

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The near-infrared excitation beams assured deep penetration depth and the label-free approach of CARS microscopy ascertained imaging of the true ELP aggregate distribution, unbiased by photodegradation and 3D diffusion properties of labeling molecules.

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For the inaccurate results, the error distribution becomes unbiased and symmetric for a time-step parameter (etale2^{-5}) and implementing a maximum level of relative energy conservation of (vert Delta E/Evert < 0.1).

Ensuring stock sustainability requires the provision of crucial information, such as species spatial distribution or unbiased abundance and precision estimates, so that management strategies can be defined (e.g. fishing quotas, temporal closure areas or marine protected areas MPA).

The system provides power with a Poisson distribution for unbiased isolation of up to 1,800 single cells on a single chip, control with CellSelect software, and insight by enabling the analysis of up to eight samples on a single chip, so that complex experimental designs can now be accomplished in single-cell research.

The Poisson distribution reflects unbiased random occurrences of Ca2+ surges using the mean counts of astrocytic Ca2+ events measured in the experiments (1.5 and 0.4 Ca2+ events/cell/30 min in L1 and L2/3, respectively).

This suggests that mean values could be assumed to have an asymptotic Normal distribution and unbiased estimators were obtained.

Starting from low mutation rates, each time a sequence is mutated, a small positive random number is added to its mutation rate (if the distribution is unbiased and negative numbers can be drawn, mutation rate decreases).

Specifically, the estimated parameters from 100 values (randomly drawn from the Gumbel distribution) were unbiased and with reasonable precision: The means were 0.02536843 and 0.2314643, while the corresponding "standard errors" were 0.002286060 and 0.002688641.

Marginal variables (i.e. the nodes with no network parents, (Coating, pH and Contamination)) are assumed to be represented by uniform distributions for unbiased parameterization when adequate knowledge is lacking [32].

However, continuous valued features can have a large range and it is hard to derive the unbiased distribution from the observed frequency distribution.

The random selection of nodes as recipients of an immunization represents an unbiased distribution of resources and is the simplest method for distribution.

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