greater accuracy than MP methods in the presence of variation in rates of evolution among characters (or across sites in a genome). However, these methods are not yet able to accommodate variation in rates of evolution over time, otherwise known as heterotachy. If the rate of evolution for a specific character accelerates on a branch of the phylogeny, then the amount of evolution that has occurred on that branch will be underestimated for a given length of the branch and assuming a constant rate of evolution for that character. In addition to that, it is difficult to distinguish heterotachy from variation among characters in rates of evolution. Since ML (unlike maximum parsimony) requires the investigator to specify a model of evolution, its accuracy may be affected by the use of a grossly incorrect model (model misspecification). Furthermore, ML can only provide a single reconstruction of character states (what is often referred to as a "point estimate") — when the likelihood surface is highly non-convex, comprising multiple peaks (local optima), then a single point estimate cannot provide an adequate representation, and a Bayesian approach may be more suitable. Bayesian inference Bayesian inference uses the likelihood of observed data to update the investigator's belief, or prior distribution, to yield the posterior distribution. In the context of ancestral reconstruction, the objective is to infer the posterior probabilities of ancestral character states at each internal node of a given tree. Moreover, one can integrate these probabilities over the posterior distributions over the parameters of the evolutionary model and the space of all possible trees. This can be expressed as an application of Bayes' theorem: where S represents the ancestral states, D corresponds to the observed data, and represents both the evolutionary model and the phylogenetic tree. is the likelihood of the observed data which can be computed by Felsenstein's pruning algorithm as given above. is the prior probability of the ancestral states for a given model and tree. Finally, is the probability of the data for a given model and tree, integrated over all possible ancestral states. Bayesian inference is the method that many have argued is the most accurate. In general, Bayesian statistical methods allow investigators to combine pre-existing information with new hypothesis. In the case of evolution, it combines the likelihood of the data observed with the likelihood that the events happened in the order they did, while recognizing the potential for