Data from: The estimation of tree posterior probabilities using conditional clade probability distributions

In this paper I introduce the idea of conditional independence of
separated subtrees as a principle by which to estimate the posterior
probability of trees using conditional clade probability distributions
rather than simple sample relative frequencies. I describe an algorithm
for these calculations and software which implements these ideas. I show
that these alternative calculations are very similar to simple sample
relative frequencies for high probability trees, but are substantially
more accurate for relatively low probability trees. The method allows the
posterior probability of unsampled trees to be calculated when these trees
contain only clades that are in other sampled trees. Furthermore, the
method can be used to estimate the total probability of the set of sampled
trees which provides a measure of the thoroughness of a posterior sample.