| 11. | In this case, a posterior probability can be calculated for each site in the alignment.
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| 12. | We want the model ( hypothesis ) with the highest such " posterior probability ".
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| 13. | However, this is not an issue for computing posterior probabilities unless the sample size is very small.
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| 14. | The posterior probability is the likelihood that the parameter is correct given the observed data or samples statistics.
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| 15. | Probabilities before an inference are known as prior probabilities, and probabilities after are known as posterior probabilities.
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| 16. | Instead of scoring for single symbols of the sequence, this method considers posterior probabilities for amino acids.
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| 17. | A common scoring function is posterior probability of the structure given the training data, like the local minima.
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| 18. | The algorithm samples from regions where the posterior probability is high and the chains begin to mix in these regions.
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| 19. | Direct maximization of the likelihood ( or of the posterior probability ) is often complex when there are unobserved variables.
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| 20. | :Recommend using Bayesian analysis to estimate the posterior probability that the bomb is in each car given the data.
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