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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.
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This method distinguished the feature selection methods that chose few versus many features.
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).
The repair method that was chosen was based on grout injections in order to fill the voids located between the concrete and the underlying aggregate layer.
And proposing a method that can choose the appropriate ranging factor η and grid spacing t in order to obtain smaller localization error will also be the subject of our further research.
The opportunistic sampling method that was chosen instead is potentially prone to selection bias.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com