W4L1 Inferring phylogeny.pdf

# Character support will tend to be recovered in most

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character support will tend to be recovered in most replicates. Clade with weak support will not How to estimate clade support Bootstrapping

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306 Bayesian Inference ! ! !"" !"#" = ! !"#" !"## ! ( !"## ) ! ( !"#" ) Likelihood Prior probability of the tree, before we collected our data Probability of the data: simply put, this is a normalizing constant that makes all the probabilities sum to 1 Posterior probability As with likelihood & parsimony, heuristic methods need to be used to find the most probable tree 307 Majority-rule consensus tree Fig. 4.30 Bayesian Inference Posterior Probability - Huge numbers of trees are sampled in a Bayesian analysis using a type of heuristic search known as Markov Chain Monte Carlo - The frequency that a node is present in a posterior distribution is its posterior probability . Values between 0.95 and 1 are good. Bayes’ Theorem - Bayesian analysis gives us the probability of the tree, whereas ML gives us the probability of the data given a tree - Why avoid Bayes’ theorem? 1. Usually we don’t know the prior probabilities - Is it OK to use a prior that is uninformative? 2. The normalizing constant in the denominator is very difficult to compute. Mathematical tricks are needed. 3. In practice, Bayesian analysis works very well, and is very commonly used to infer phylogeny 308
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• Spring '18
• MORRIS
• outgroup, tree making

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