Sentence examples for variance scenario from inspiring English sources

The RB ReDif-PF algorithm RMS error performance was also compared in the unknown variance scenario to a local cooperation scheme in which each node assimilates all available measurements in its neighborhood but does not exchange its posterior statistics with other nodes.

When assuming no common environmental variance (Scenario 2), the pedigree-based analyses were also unbiased but they were less precise than the genomic IBD analyses.

Even with the stringent false-positive collar of 0.0004, under the most pessimistic variance scenario, the estimated sample size needed for each group was 42.

In other words, we generated the rth data replication for area i, Y r) = (Y i1(r),…, YiT r))′, from Note that because the aggregated number of cases in each square is the same for all replications, a pure spatial model fitted to the time-aggregated data will give exactly the same results regardless of the variance scenario.

As expected, the values of τ1 are quite small in all variance scenarios and proportion cases, because the predictable part is the true model for the unmodified areas.

However, there can be considerable bias in the estimate of variance components in case of misspecification of the random effects distribution, thereby making it difficult to distinguish between the small or large variance scenarios [ 35].

The high-variance scenario estimates the values of τ2 well: The true value 1.5 is within the average 95% credibility interval.

We consider three different cases for the amount of noise: a reference case with no noise (ω = 0), a medium-variance scenario with a medium amount of noise [ω = (0.5)], and a high-variance scenario with a high level of noise [ω = (1.5)].

The three frequently encountered scenarios are: Hozo et al.'s method only addressed the estimation of the sample mean and variance under Scenario while Scenarios and are also common in systematic review and meta-analysis.

NPMVS and limma are competitive in the three mean-variance scenarios except case 0, in which limma displayed higher type I and II error rate than the other two methods.

However, most probably by increasing the genome size and keeping all other parameters constant (i.e. SNP density, training population size and values of variance components), Scenario 6 would be at a disadvantage over Scenario 5 due to the greater number of effects that need to be predicted.