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The random fatigue limit model of Pascual and Meeker and two proposed simplified models (bilinear and hyperbolic) used maximum likelihood estimation techniques to fit probabilistic stress-life curves to experimental data.
We used maximum likelihood estimation, which is the most commonly used supervised classification method in the field, as implemented in the ENVI software.
We used maximum likelihood estimation for discriminating between a power-tailed distribution and the exponential alternative and rank/frequency plots to discriminate between Lévy walks and composite Brownian walks.
We used bootstrapping to fit psychometric curves to each participant's data and to determine individual 50% perceptual subjective equality or PSE thresholds with 95% confidence interval limits and we used maximum likelihood estimation (MLE) to fit psychometric curves to data averaged across all participants [15], [16].
51 All the analyses used maximum likelihood estimation.
All factor analyses were conducted on polychoric correlation matrices and used maximum likelihood estimation.
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Equation 4 was estimated using maximum likelihood estimation and the estimated results are summarized in Table 3.
We can use maximum likelihood estimation algorithm to estimate the Gaussian parameters for each class from training points.
All models were fitted using maximum likelihood estimation which calculates estimates of the statistical parameters most likely to have produced the observed data.
It is estimated using maximum likelihood estimation technique.
The OLR model was estimated using maximum likelihood estimation method.
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