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The coalescence time distributions were fitted by the stochastic model.
Mass distributions were fitted by Gaussian distribution and mass variance extracted.
The vertical distributions were fitted by a two-parameter right truncated Weibull distribution.
Thus obtained 50 breakage force distributions and corresponding 50 breakage work distributions were fitted with log-normal distribution function.
Power law distributions were fitted using the Python package powerlaw [27].
Beta distributions were fitted for the parameters that described transition probabilities and health -related utilities, and gamma distributions were fitted for parameters that described costs [37].
Similar(27)
Weibull (2 parameter, 3 parameter) and lognormal distributions were fit to fatigue life predicted using viscoelastic continuum damage approach.
Open time distributions were fit with a single exponential; up to three exponentials were fitted to dwell-time distributions.
These distributions were fit to a Poisson distribution, and the mean of the distribution (lambda) was calculated.
Gene family size distributions were fit to power laws to compare gene duplication trends in pathogens versus non-pathogens.
Generalized linear models based on negative binomial distributions were fit to the data using edgeR, and the model includes treatment and replicate effects.
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