| 1. | Posterior probability is a conditional probability conditioned on randomly observed data.
|
| 2. | MrBayes uses MCMC to approximate the posterior probabilities of trees.
|
| 3. | Bayes theorem then shows that the posterior probabilities are proportional to the numerator:
|
| 4. | As such, other methods have been put forwards to estimate posterior probability.
|
| 5. | Taking the posterior probability and determining the prior probability.
|
| 6. | What is important is the relationship between the loss function and the posterior probability.
|
| 7. | This contribution is called the posterior probability and is computed using Bayes'theorem.
|
| 8. | Posterior probabilities are after a fact is known.
|
| 9. | The same posterior probability result can be obtained if one uses an un-normalized prior
|
| 10. | One way to achieve this goal is to provide a credible interval of the posterior probability.
|
मोबाइल