The implementation of polymorphism-aware trait evolution in a Bayesian framework provides a new, flexible way to model evolutionary processes and obtain reliable strengthen estimates of biological parameters. The PhD project will couple the approach with numerical methods––such as Markov chain Monte Carlo (MCMC)––for approximating the posterior probability distribution of parameters. Bayesian inference methods can be extremely powerful and have revolutionized the range of evolutionary questions that can be tackled. In particular, the Bayesian framework allows us to integrate different types of data: the molecular sequence data and (importantly) the phenotype/trait data.
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