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In summary, the approaches we described use a developed unified dynamic test framework that includes SETI with statistical significance testing, ranking temporal genes by AR(1) modeling and posterior probability of autocorrelation parameter, and HMM to classify temporal dynamic patterns.
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Prior and posterior distributions for the model parameters.
The gain yielded by a dataset is given by the relative entropy (also known as Kullback Leibler divergence) between prior and posterior probabilities (Baldi and Itti, 2010; Liepe et al., 2013) (9) In the context of modeling, the relative entropy has a precise interpretation based on the analogy between learning and communication.
Anterior and posterior views of the liver.
Well-marked wrinkles grooves along the dorsal and posterior margin.
Prior and posterior probability computation of the scRNA-seq data.
Dorsal side is up and posterior is to the left.
Anterior and posterior commissure.
Targets differentially activated middle frontal gyrus, posterior parietal cortex, and posterior cingulate gyrus.
In addition, a surgical approach modifier is added (posterior approach or combined anterior and posterior approaches).
Angle between the posterior thigh and posterior calf.
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