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An LMI-based optimization problem is then derived for computing the state feedback gains maximizing the estimate of the stability region of the closed-loop system.
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In this section, we introduce our method to estimate the parameters in model (2.1) based on maximizing the estimated partial likelihood.
The number of populations that best represents the observed data under the model implemented was determined by maximizing the estimated Ln likelihood of the data for different values of K. Third, we used the Bayesian clustering algorithm implemented in Baps 4 [71] to identify the optimal number K of partitions among groups of samples.
The group-specific distance between two signatures of genomes from a group is given by the following: (6) where M is a diagonal matrix learned by maximizing the estimated generalization performance with training data from the same group (as x and y).
Then the parameters β can be estimated by maximizing the following estimated partial likelihood function (EPL): EPL beta)=prod_{i=1}^{n}left{frac{hat{r}_{i}(beta,S_{i})}{sum_{jin {mathcal R}(S_{i})}hat{r}_{j}(beta,S_{i})}right}^{delta_{i}}.
The segmentation was performed by maximizing the autonomy estimate.
The unmixing matrix is estimated by maximizing the statistical independence of the estimated components.
Then, all possible combinations of the joinpoints were tried to evaluate the best‐fitting number of joinpoints to maximize the log‐likelihood estimate.
The parameters δ W and δ H in the MRF models can be estimated using standard marginalization procedures or by maximizing the Type II ML estimate for (10).
As estimates of α, β, we use the values maximizing the marginal likelihood given the estimates, and.
The common target of all the studies is to demonstrate the possibilities of maximizing the reliability of parameter estimates for the limited experimental sets of data.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com