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These binding probabilities were then used to identify condition-dependent changes in binding.
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The model probabilities were then used to form model probability weighted averages of the parameters.
These prior probabilities were then used to compute the posterior probabilities of of candidate regression models.
These probabilities were then summed for every hospital.
These probabilities were then used for NNT calculation.
These posterior probabilities are then used to make better decisions.
The individual criterion probabilities are then combined to obtain an estimate of overall probability.
For the Viterbi Algorithm, the binding probabilities are set as the transition state probabilities, while the torsion state probabilities are set as the emission probabilities.
A joint probability is then determined for each alternative outcome by multiplying the prior probability by all conditional probabilities.
The transition probability is then the product over the segment probabilities.
The voxels in this probability image were then ranked from N (highest probability) to 1 (lowest probability), giving voxels of equal probability a mean rank.
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