Sentence examples for denoted model from inspiring English sources

This Appendix reports results from an alternative "value-added" model which conditions the current outcome on the last outcome (denoted model (4)).

In the first model considered (denoted model 1 hereafter) and according to Nicholson et al. [17], the second step assumes that the αij are sampled from a truncated Gaussian distribution on the (0,1) segment <img src="http://journals.plos.org/plosone/article/asset?id=info?doi/10.1371/journal.pone.0011913.e002.PNG" class= inline-graphic"/> plus additional probability masses at 0 and 1.

The model allowing for positive selection is denoted model A and the lineage to be tested is the foreground lineage, whereas the remaining ones are the background lineages; the multiple hypothesis testing problem [ 47] was taking into account using Bonferroni's correction [ 48].

The study demonstrated that GLAS data could improve large-scale monitoring of aboveground biomass at large spatial scales; however, the presented estimators were not denoted "model-assisted".

In a study by Ståhl et al. (2011) it was simply called model-based inference, although later denoted model-dependent estimation by Gobakken et al. (2012).

Here, x denotes the 'active' inputs that we seek to determine from model inversion, and 𝜃 denotes model parameters that remain fixed.

"M1" denotes model 1. denotes the homozygous major allele genotype of SNP A; denotes the heterozygous genotype of SNP A; denotes the homozygous minor allele genotype of SNP A; likewise for the notations in the other basic models.

In regard to the notation, we shall use capital letters to denote model parameters and random variables, and small ones for the realizations of these variables.

Now consider a second model, which we denote model 0, or H0, in which the additive genetic effects are assumed to account for only 5% of the phenotypic variance.

The term 'relative survival' used to denote model (A1) derives from the implied relation between the cumulative survival of a cancer subject and that of the equivalent reference subject.

This quantity is given by the formula: (24) var P [ E(Y/P) ] var(Y) = D P D where Y denotes model output and P denotes the vector of model parameters; DP and D illustrate the partial and total variance of the model output due to variation in model parameters according to assigned statistical distributions.