Probabilistic methods draw on an explicit model of state evolution, permitting the ability to glimpse the complete state history over the entire phylogeny and conveniently draw statistical inferences [15] [16] [17] . These analyses typically employ continuoustime Markov chain models for discrete state evolution analogous to common nucleotide, codon or amino acid substitution models [18] . In contrast to parsimony, maximum likelihood-based reconstructions incorporate branch length differences in calculat-ing the conditional probability of each ancestral state given the observed states at the phylogeny tips [14] . Bayesian reconstruction methods enable further generalization of this conditional probability analysis by removing the necessity to fix the Markov model parameters to obtain ancestral states and the necessity to specify a fixed tree topology with known branch lengths. Bayesian inference integrates conclusions over all possible parameter values but to achieve this, however, requires prior probability distributions for all aspects of the model. While probabilistic methods have been previously presented in a bio-or phylogeographic context, in particular Bayesian methods that integrate over phylogenetic uncertainty and Markov model parameter uncertainty [19] , viral phylogeography studies have rarely made use of these developments. This may be a consequence of low awareness of existing software implementations for arbitrary continuous-time Markov chain models [20, 21] or a lack of appreciation for the uncertainty intrinsic in these reconstructions and the ease with which one can formally access epidemiological linkage through probabilistic approaches. A recent phylogeographic study of influenza A H5N1 introduces a heuristic non-parametric test to evaluate whether parsimonyinferred migration events between two particular locations occur at significantly high frequency [22] . Null distributions for these frequencies arise from randomizing tip localities after false discovery rate correction to control for simultaneous testing issues. Although this procedure addresses concerns about statistical inference on sparse frequency matrices, the multiple comparison correction still results in a conservative estimate of significant migration events. Fully probabilistic approaches may further ease statistical inference, yet similar tests remain lacking for likelihoodbased phylogeographic models.