The particle filter calculates with each measurement the updated particles { x t ( i ), q t ( i ) } i = 1 N p and their weights { w t ( i ) } i = 1 N p from the corresponding previous particles { x t - 1 ( i ), q t - 1 ( i ) } i = 1 N p and weights { w t - 1 ( i ) } i = 1 N p. A single step of the bootstrap version of the MMPF is summarized below.
When fewer potential predictors are retained after the first step of the bootstrap model selection procedure, this variability seemed to decrease and model selection frequency increased.
Parameters, including sliding window size, step size, bootstrapping iterations, and the use of trimA1 (version 1.2) [ 37] to remove gaps, are adjustable.
Another way to take parameter uncertainty into account is by introducing an initial step of non-parametric bootstrapping into the bootstrap sample generation, as done e.g. in [ 11].
Tree ensemble used for AnGST was generated by the non-parametric bootstrapping step of PhyML (1,000 bootstrap replications) (http://www.atgc-montpellier.fr/phyml/).
In the second step, we bootstrapped only codon bias of genes for the same number of times (5000) while keeping intron content unaltered.
In the next step of analysis, a bootstrapping method was used to make conclusions about the significance of indirect effects of social cohesion through social embeddedness, tangible support, and emotional support towards well-being.
Hence, 풞 = { C 1, …, C K } correspond to these individual network components.In the second step, an ensemble of bootstrap data sets { D i B } i = 1 b is generated from D, as described in Figure 2, from which we infer an ensemble of networks { G i B } i = 1 b, one for each bootstrap data set.
For the interpretation of the results it is most important that all modeling steps are repeated in each bootstrap sample and that the same set of bootstrap samples is used for all strategies.
