posterior probability

This did not result in changes of the estimated posterior probabilities.

The posterior probability of project failure, calculated in the same way, would be 0 13.

The posterior probability might also provide information about possible misclassifications.

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.

Finally, each data point is assigned to the component with the largest posterior probability.

The posterior probability values changed by less than 0.3% from case to case.

Second, we tried different numbers of cycles to calculate posterior probabilities.

Again, the posterior probability values did not change significantly from case to case.

Thus calculating a posterior probability for all such maps is infeasible.

In this case, a posterior probability can be calculated for each site in the alignment.

The posterior probabilities were plotted to equilateral triangle as described above.

However, this is not an issue for computing posterior probabilities unless the sample size is very small.

The remaining 20,000 cycles were used to calculate posterior probabilities for each of the three tree topologies.

The posterior probability of given is high, indicating strong evidence that is in better agreement with than .

From these values, we find the posterior probability of , which strongly favors over .

In some figures, we use a phase diagram representation of the same information in the three posterior probabilities.

For calculation of posterior probabilities with MrBayes at least 25,000 cycles were used.

This additional information can be used to reduce the level of uncertainty in the project, producing the posterior probability of the same outcome.

For example, in Bayesian inference, a posterior probability distribution is calculated as the product of two distributions.

Calculate the posterior probabilities and all labels .

Variational Bayes will then construct an approximation to the posterior probability .

This contribution is called the posterior probability and is computed using Bayes' theorem.

This output distribution is a "posterior probability distribution".

The evidence (also termed normalizing constant) may be calculated since the sum of the posterior probabilities must equal one.