Sentence examples for producing posterior from inspiring English sources

To evaluate in a prospective study the efficacy of intrasurgical autologous plasmin enzyme (APE) in producing posterior vitreous detachment.

An example analysis is presented using wolverine data collected in southern Alaska, and this example demonstrates how to summarize posterior output for the purposes of producing posterior density maps and computing derived quantities such as the effective sample area.

At the early larval stages, most seam cells, including T.a, V6.pa, and V6.pp, undergo asymmetric divisions producing posterior seam cell (Se) daughters and anterior daughters, which are terminally differentiated cells that fuse with the hypodermal syncytium (Sy) within a few hours after they are born (Figs. 5A and B) [25].

Because BEAGLE produces posterior genotype probabilities for imputed genotypes, when the number of ungenotyped SNPs is increased, the amount of calculation, including sampling haplotypes and producing posterior genotype probabilities, is correspondingly increased.

The second part deals with model uncertainty by employing the product space formulation of transdimensional MCMC (S isson 2005) as described in section 2.5 of G odsill (2001), which facilitates Bayesian model selection by producing posterior probabilities of the microsatellite mutation models and Bayesian model averaging for estimates of population history and genealogies over those models.

The equalizer again produces posterior information L E ( s ̂ ) of the interleaved coded symbols.

Like the MAP equalizer, the MAP decoder produces posterior probabilistic information on the source symbols.

The theorem shows how, through the likelihoods, evidence combines with prior probabilities (prior plausibility assessments) to produce posterior probabilities (posterior plausibility values) for hypotheses.

The native medial meniscus proved to be a dynamic structure under load as initial heel strike produced posterior translation and superior displacement of the meniscus.

Though many supervised machine learning algorithms have the ability to coerce classification distribution, the NB machine learning algorithm naturally produces posterior probabilities without coercion [19].

When a given classification algorithm produced posterior probabilities of mutation status, we assigned each variant's mutation status according to the higher posterior probability (Weka's default behavior).