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Boli'c [6] proposed architecture for distributed resampling with proportional allocation.
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The scheme termed distributed resampling with non-proportional allocation (DRNA) for the parallelisation of PFs was originally introduced in [2] (Section IV.A.3), but it has been only recently that a theoretical characterisation of its performance has been obtained [10,11,26].
PF is implemented on the GPU with a distributed resampling.
Several efforts were expended to construct distributed resampling algorithms [1, 2].
This method is motivated by the distributed resampling with non-proportional allocation (DRNA) proposed by Bolic et al. [20].
The CU is designed to support both the distributed resampling algorithm with proportional allocation (RPA) and non-proportional allocation (RNA).
In this paper, we investigate two classes of such techniques, distributed resampling with non-proportional allocation (DRNA) and local selection (LS).
Two algorithms for distributing the resampling procedure, namely resampling with proportional allocation (RPA) and resampling with nonproportional allocation (RNA), have been proposed in [49] where the sample space is divided into several groups and each PE is in charge of processing one such group.
In this section, we discuss the different resampling algorithms for distributed PFs.
Have a plan for distributing the story.
A total of 999 bootstraps were conducted for each species during which replicate transect lines, assumed to be independently and identically distributed, were resampled at random and with replacement until each bootstrap resample was the same size as the original number of transects.
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