It was the introduction of Markov Chain Monte Carlo (MCMC) methods by Nicolas Metropolis in 1953 that revolutionized Bayesian Inference and by the 1990s became a widely used method amongst phylogeneticists.
This tutorial introduces the BEAST software for Bayesian evolutionary analysis through a simple tutorial.
The package also provides new tools for visualizing and summarizingmultispecies coalescent and phylogeographic analyses.
Bayesian inference of phylogeny uses a likelihood function to create a quantity called the posterior probability of trees using a model of evolution, based on some prior probabilities, producing the most likely phylogenetic tree for the given data.
Independently, unaware of Bayes work, Pierre-Simon Laplace developed Bayes' theorem in 1774.
Bayesian inference was widely used until 1900s when there was a shift to frequentist inference, mainly due to computational limitations.
This is done using the program BEAUti (which stands for Bayesian Evolutionary Analysis Utility).
BEAST is fast, flexible software for Bayesian analysis of molecular sequences related by an evolutionary tree.
The Bayesian approach has become popular due to advances in computing speeds and the integration of Markov chain Monte Carlo (MCMC) algorithms.
Bayesian inference has a number of applications in molecular phylogenetics and systematics.
It can be used as a method of reconstructing phylogenies but is also a framework for testing evolutionary hypotheses without conditioning on a single tree topology.
BEAST uses MCMC to average over tree space, so that each tree is weighted proportional to its posterior probability.