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The likelihoods (both of the tree and coalescent model) should have decent ESSs. At the moment we don't have anyway of directly examining the ESS of the tree or the clade frequencies.
Therefore, it is important that the continuous parameters and likelihoods have adequate ESS to demonstrate good mixing of the MCMC. Tuning the operators will only increase the efficiency of the sampling - resulting in better ESSs for the same chain length.
In BEAUti, on the MCMC tab, click the checkbox 'Sample from prior only - create empty alignment', save the XML and run with BEAST.
Alternatively, in the XML file, remove (or comments out) the entries in the The simple answer is that you may not want to - BEAST will sample the root position along with the rest of the nodes in the tree.
However, it is not necessarily going to benefit all data sets.
In particular, for the use of a GPU to be efficient, long partitions are required (perhaps The tree produced by Tree Annotator (denoted the maximum clade credibility or MCC tree) is not a consensus tree such as that produced by the 'sumt' command in Mr Bayes.
If you then calculate the proportion of trees that have a particular root, you obtain a posterior probability for this root position.
In general using it (even if not using a GPU) will improve the performance of BEAST.This means the average heights are for the adjacent nodes are derived from different sets of trees and may not have any direct ancestor-descendent relationship.The Effective Sample Size (ESS) of a parameter sampled from an MCMC (such as BEAST) is the number of effectively independent draws from the posterior distribution that the Markov chain is equivalent to.MCC trees produced by Tree Annotator can have a descendent node that is older than its direct ancestor (a negative branch length).
This may seem like an error but is actually the correct behaviour.
operator should be proportional to the height of your tree (say about 10% initially).