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This method distinguished the feature selection methods that chose few versus many features.
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They have analyzed datasets from a variety of elections, showing that many of the usual voting methods that are considered irreconcilable (e.g., plurality, Borda count and methods that choose the Condorcet winner) are, in fact, in perfect agreement.
Methods that choose animals that have the closest average relationship or contribution to the target population gave the lowest accuracy of imputation, in some cases worse than random selection, and should be avoided in practice.
Approaches like ours are also well-suited for imputation in recently admixed populations: methods that choose custom reference panels for different admixed individuals in different parts of the genome can increase accuracy by adapting to local ancestry changes, as previously suggested by Pasaniuc et al. (2010).
From the Bayesian view point, MAP criterion is an estimation method that chooses an estimation of unknown variable which could maximize the posterior probability distribution function, i.e., θ ^ = arg max θ p θ | y (22).
The concept can be used with any iterative selection method that chooses a trial design for each iteration, and uses the DM's preference parameters at that trial design to eliminate some design options which have lower value than the trial design.
Hence, in this paper we develop models for MD streaming over multiple paths and based on these models we propose a multi-path selection method that chooses a set of paths maximizing the overall quality at the client under various constraints.
In contrast, an equivalent search method that chooses clusters of sites using default parameters leads to an iterative and time-consuming process of optimization.
Had these studies genotyped their reported isolates or genotyped them using methods that we chose to include, they might have altered the results of the study.
In addition, we compared the results of the proposed method with results obtained using other older well-established early fault detection methods that were chosen from 4 groups of methods which were classified by the domain of observation.
In the present study, marker solutions were obtained according to three different methods that were chosen on the basis of their statistical properties, treatment of the marker effect and correction for population stratification.
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