Bayesian inference in phylogeny
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Bayesian inference in phylogeny generates a posterior distribution for a parameter (composed of a phylogenetic tree (its branch lengths and topology) and a model of evolution) based on the prior for that parameter and the likelihood of the data (generated by a multiple alignment). The Bayesian approach has become more popular due to advances in computational machinery, especially, Markov Chain Monte Carlo algorithms. Bayesian inference has a number of applications in molecular phylogenetics, for example, estimation of species phylogeny and species divergence times.