This additional information can be used to reduce the level of uncertainty in the project, producing the posterior probability of the same outcome.
The posterior probability of project failure, calculated in the same way, would be 0.13.
In this case, a posterior probability can be calculated for each site in the alignment.
Thus calculating a posterior probability for all such maps is infeasible.
The posterior probability might also provide information about possible misclassifications.
Finally, each data point is assigned to the component with the largest posterior probability.
However, this is not an issue for computing posterior probabilities unless the sample size is very small.
We want the model (hypothesis) with the highest such posterior probability.
A common scoring function is posterior probability of the structure given the training data.
Second, we tried different numbers of cycles to calculate posterior probabilities.