There's still something that confuses me about how many of these algorithms work, and how results are presented in the literature.
Let's consider a Maximum Likelihood based algorithm like MrBayes or RAxML: Users set a random number seed which generates the starting tree. For many of our datasets, different seeds which result in different ML results, as the algorithms are initialized with different trees.
I'm not entirely sure how one is supposed to interpret this, especially as my experience with ML methods is that the initial step is irrelevant to the global/local min/max in parameter space----the chains just take longer to converge.
How should one interpret these results? Are users to run 1000s of trees at different parameter values, and then chose the most optimal likelihood value? That seems rather ad hoc, as does bootstrapping, etc.
Is the dataset fundamentally flawed?