Given our parameter ranges and data, the maximum entropy condition implies unimodal distributions on the parameter grid.
Given our experimental parameters, the prior probabilities for each orientation, P(O i ), and the normalizing factor, P(S), are both constants that can be removed without affecting the relative orderings of P(O i |S).
The Hahn and Shapiro catalogue was used to select combinations for an orthogonal main effects design, and to organize the selected profiles into the most D-efficient choice design, given our design parameters.
Given our mapping parameters, several regions of the assembled sequence remained devoid of Illumina tags in the first cycle but became covered with Illumina tags after sequence correction and iteration of the process.
Given our permissive parameters for designating AEI and the possibility that gene homology is contributing to overrepresentation of AEI, we increased the stringency for designating AEI based on the variability of allelic ratios between SNPs in the same gene, to ask which genes have strong evidence for harboring cis-acting regulatory variants.
However, SOAPindel is limited in the size of detectable insertions, depending on the insert size of the reads: given our simulation parameters, we observed that SOAPindel recall decreased for insertions larger than 175 bp, and the largest insertion detected was of length 189 bp.
To predict if Wolbachia can invade the current population, given our estimated parameter values, we ran 10000 bootstraps to estimate the means and 95% CIs of F* 1-μ) and p2, setting μ = 0 iF* 1-μs estimandd to be less than zero in any individual bootstrap2
To check the effect of topography, we calculated the displacement of the three JMANET stations, given our estimated source parameters, using the finite element method (FEM) and a digital elevation model (DEM).
Given our approximation (16), these parameters have to be inferred for each new image, in a similar fashion as for PLSA [22]. is estimated by maximizing the likelihood of the patch histogram of, fixing the learned parameters in the maximization step.
In a Bayesian approach, we base our conclusions about the probability of a value for a parameter given our data, Pr(θ|y), on a combination of our prior expectation for the value of that parameter, expressed as the probability of observing the parameter Pr, and the likelihood of observing the data we have collected, Pr y|θ) given that parameter: Pr θ | y ∝Pr y | θ * Pr θ, (1).
For the pedigree and within-family design, the parameter given our model assumptions is the narrow-sense heritability.
