Bayesian inference in phylogeny generates a posterior distribution for a parameter, composed of a phylogenetic tree 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.
Read more about Bayesian Inference In Phylogeny: Basic Bayesian Theory, The LOCAL Algorithm of Larget and Simon, Metropolis-coupled MCMC (Geyer)
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