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We use regression analysis assuming normally distributed errors (minimizing sums of squares) and Poisson distributed errors (maximizing Poisson log-likelihood).
The fit for each model to observed counts of observed waterbirds is performed using regression analysis, minimizing sums of squares by assuming both normally distributed and log-normally distributed errors.
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Lasso applies linear discriminant analysis with minimizing sum of regression coefficients.
Many deformable registration methods minimize sum of squared difference (SSD) as the registration cost with respect to deformable model parameters.
A firm wants to minimize sum of discounted total cost of satisfying demands for a single input.
The main objective of Function (12) is to minimize sum of the total transportation cost for the vehicles of coalition S m.
The method finds the optimal design of the vehicle by iteratively using dynamic programming (DP) and convex optimization to minimize sum of operational and component costs over a given driving cycle.
Kinetic and transport parameters are estimated by minimizing the sum-of-squares error between the model and experimental measurements using a simplex method.
The model was calibrated to fit the initial epidemic curve observed in Ontario (by minimizing the sum-of-squares difference between model projections and the observed epidemic curve).
Data extraction and fitting was achieved by floating all parameters and minimizing the sum-of-squares error term in an ExcelTM spreadsheet.
Model parameter estimates were obtained (using the fmincon function in MATLAB) by minimizing the sum-of-squared-errors objective function.
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