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The distributions of switch latencies were fit with a mixture of Gaussian distributions (parameters were chosen with a maximum likelihood estimation of the empirical data) as we have previously described4,23.
The descriptive statistics of failure and repair data, the identification of the most important failure modes, and the determination of the theoretical distributions parameters that best fit to failures data was carried out.
Specifically, we can estimate the distributions parameters by maximizing the logarithm of the likelihood l of sample residuals.
Exposure groups were simulated based on distributions parameters as shown by N representing a normal and log-N a log-normal distribution.
We conducted partitioned analyses with variable rates (ratepr = variable); and unlinked base frequencies, transition/transversion rate ratios, rate matrices, gamma distributions parameters between individual partitions, and sampled trees and parameters every 1000 generations from four chains (one unheated).
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We note that in Kum-Par as well as Kum-Pow distributions parameter-dependent boundaries exist.
The parametric approach relates the normal range of data with distributed range to compute distribution parameters.
Random sampling, estimation of distribution parameters (method of moments, maximum likelihood, Bayesian estimation), and simple and multiple linear regression.
The distribution parameters were obtained using three different methods.
Calculated molecular weight distribution parameters were similar to measured data.
For estimating the distribution parameters, a statistical methodology called ProGumbel has been developed.
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