Sentence examples for sampling as implemented from inspiring English sources
Additionally, we tested for correlation between dimorphism and stomatal complexity by Bayesian MCMC sampling as implemented in BayesTraits V2 (beta) (Pagel and Meade, 2013).
The biological model parameters were estimated using stochastic approximation expectation maximization followed by importance sampling as implemented in NONMEM 7.3.0 (ICON Development Solutions).
Bayes factors were calculated from the marginal likelihoods by importance sampling, as implemented in Tracer (v1.4.1) (R ambaut and D rummond 2007), using the method of S uchard et al. (2001).
The stochastic approximation expectation maximization method with importance sampling as implemented in NONMEM was used to obtain the estimates of standard errors for the biological models in our analysis, because of numerical difficulties with the first-order conditional estimation method.
Oasis supports multivariate differential expression analysis of samples as implemented in the DESeq2 (Love et al., 2014) package.
Variable scalings were determined from the training samples, and applied to the test samples, as implemented in the opt.nested.crossval function of the pensim R package.
We used the Gibbs sampler as implemented with WinBUGS 1.4 software to estimate the model parameters, with 95% credible intervals (CrI) (Gelman et al, 1995).
Departure from Hardy-Weinberg expectations (HWE) was tested with 1000 permutations using a global test across loci or samples as implemented in GENEPOP v. 4.2 [ 46].
Finally, a number of SNP probes are also included on the Infinium array that can help identify mislabelled samples, as implemented in wateRmelon (Pidsley et al, 2013).
We computed pairwise FST between all populations for each unigene using a bias-corrected estimator for pooled population samples as implemented in PoPoolation2 (version 1.201; [ 37]), again using a minimum coverage of 5 and a maximum coverage of 40 for each SNP locus.
Principal component analysis on the entire gene expression data set of 326 CRC samples, as implemented in the Princomp function in Matlab, (Mathworks Inc ., was computed by selecting the 1st principal component (PC1) corresponding to the highest eigenvalue of the covariance matrix, describing the inherent variability of the data.
