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In brief, the Bayesian inference of each genotype is based on its posterior distributions, P(G| D), using Bayes' formula.
In the mixed model option, a specific model is not specified a priori, but each model is chosen during the run based on its posterior probability.
Next, each edge (u, v) is weighted based on its posterior probability of being a co-complex edge (i.e. both u and v are in the same complex), given the scores of the data source relationships between u and v.
Finally, we summarized the posterior distribution by calculating a statistic based on its posterior mean divided by its posterior standard error (Typified Bayes Value, TBV, see below; Interpretation of the Bayesian Values).
Next, each edge is weighted based on its posterior probability of belonging to a protein complex, using a naive-Bayes maximum-likelihood model learned from a set of training complexes.
Next, we weight each edge of the composite network based on its posterior probability of belonging to a protein complex, using a naive-Bayes maximum-likelihood model learned from a set of training complexes.
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Based on the Bayesian posterior probability of influenza-like illness (ILI) visits, the intensity of outbreak can be directly assessed.
In macaque, the anterior insula (aI) has been dissociated from the middle and posterior insula based on its cytoarchitecture and connectional anatomy.
To resolve some of the deficiencies of anterior lumbar arthroplasty, such as the approach itself, difficulty of revision, and postoperative facet pain, 360° motion preservation systems based on posterior disc and posterior dynamic system (PDS) designs are being pursued.
Intuitively, for the data carrier X ̂ ( k ), we can measure its reliability based on relative posterior probability that the difference term D k) equals X ̂ ( k ) − 〈 X ̂ ( k ) 〉 to the probability that it equals some other vector X ̂ ( k ) − Ω m | Ω m ≠ 〈 X ̂ ( k ) 〉.
The first one is based on the posterior probability computed by the model and the other is based on the derivative of the ROC function of the model.
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