error and uncertainty. Overall, it is the most accurate method for reconstructing ancestral genetic sequences, as well as protein stability. Unlike the other two methods, Bayesian inference yields a distribution of possible trees, allowing for more accurate and easily interpretable estimates of the variance of possible outcomes. We have given two formulations above to emphasize the two different applications of Bayes' theorem, which we discuss in the following section. Empirical and hierarchical Bayes One of the first implementations of a Bayesian approach to ancestral sequence reconstruction was developed by Yang and colleagues, where the maximum likelihood estimates of the evolutionary model and tree, respectively, were used to define the prior distributions. Thus, their approach is an example of an empirical Bayes method to compute the posterior probabilities of ancestral character states; this method was first implemented in the software package PAML. In terms of the above Bayesian rule formulation, the empirical Bayes method fixes to the empirical estimates of the model and tree obtained from the data, effectively dropping from the posterior likelihood, and prior terms of the formula. Moreover, Yang and colleagues used the empirical distribution of site patterns (i.e., assignments of nucleotides to tips of the tree) in their alignment of observed nucleotide sequences in the denominator in place of exhaustively computing over all possible values of S given . Computationally, the empirical Bayes method is akin to the maximum likelihood reconstruction of ancestral states except that, rather than searching for the ML assignment of states based on their respective probability distributions at each internal node, the probability distributions themselves are reported directly. Empirical Bayes methods for ancestral reconstruction require the investigator to assume that the evolutionary model parameters and tree are known without error. When the size or complexity of the data makes this an unrealistic assumption, it may be more prudent to adopt the fully hierarchical Bayesian approach and infer the joint posterior distribution over the ancestral character states, model, and tree. Huelsenbeck and Bollback first proposed a hierarchical Bayes method to ancestral reconstruction by using Markov chain Monte Carlo (MCMC) methods to sample ancestral sequences from this joint posterior distribution. A similar approach was also used to reconstruct the evolution of symbiosis with algae in fungal species (lichenization). For example, the Metropolis-Hastings algorithm for MCMC explores the joint posterior distribution by accepting or rejecting parameter assignments on the basis of the ratio of posterior probabilities.