** Next:** Appendices
** Up:** Bayesian phylogenetics
** Previous:** Proposals for the tree
** Contents**

One can doubt that maximum-likelihood algorithms always find the
true global maximum of the likelihood function. Similarly, with MCMC
techniques, the Markov chain can fail to converge to the stationary
distribution of the posterior probabilities. A possible reason for this
is the failure to visit all highly probable regions of the
parameter space because of local maxima in the likelihood curve.
However poor proposal mechanisms and/or failure to run the
chain long enough are usually the main cause of sample defect
(see Huelsenbeck et al., 2002).
Unfortunately it is not always easy to identify these traps.
We can only recommend to do long runs, monitor the
convergence of **several** model parameters since monitoring the
likelihood only is not enough, and repeat the experiment using different
random starting trees to check that all the chains give similar results
(*i.e.*,
substitution model parameters, consensus tree, likelihood, ...).

Gowri-Shankar Vivek
2003-04-24