Bayesian Divergence Time Estimation with Markov Chain Monte Carlo
Explore the goal of Bayesian divergence time estimation using MCMC to simplify calculating marginal probabilities. Learn about parametric distributions for node age priors and tools like BEAUti, Tracer, LogCombiner, and TreeAnnotator. Engage in practical divergence time estimation using BEAST v2.
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Presentation Transcript
the goal of Bayesian divergence time estimation Markov chain Monte Carlo (MCMC) to eliminate the difficult task of calculating the marginal probability of the data.
Five different parametric distributions that can be applied as priors on the age of a calibrated node.
BEAUti BEAUti is a utility program with a graphical user interface for creating BEAST and *BEAST input files which must be written in the eXtensible Markup Language (XML). Tracer Tracer can be used for visual inspection and assessment of convergence and it also calculates 95% credible intervals and effective sample sizes (ESS) of parameters. LogCombiner LogCombiner can be used to combine the parameter log files or tree files into a single file that can then be summarized using Tracer (log files) or TreeAnnotator (tree files). TreeAnnotator TreeAnnotator is used to summarize the posterior sample of trees to produce a maximum clade credibility tree.
Practical: Divergence Time Estimation using BEAST v2.* Dating Species Divergences with the Fossilized Birth-Death Process Download data and pre-cooked output files from: http://treethinkers.org/divergence-time-estimation-using-beast bears_irbp_fossils.nex and bears_cytb_fossils.nex
.nex .xml .log .trees .combined.trees .summary.tree
