Bayesian inference of time trees

Introduction

  • Bayesian inference tries to obtain a probability distribution rather than a single best fit c.f. maximum likelihood
  • This is typically obtained using numerical approaches such as Markov Chain Monte Carlo
  • For example, Metropolis sampling involves the following
    • Make a change to the parameters
    • If the new parameters result in a better fit, change to them
    • If the new parameters result in a worse fit, move to them with a probability based on the drop in fit
    • Repeat many times

Strict and molecular clocks

  • In phylogenetic inference, Bayesian approaches have become popular to fit molecular clocks to sequence data
  • Two commonly used platforms
    • MrBayes
    • BEAST
  • Both use the same underlying principles, but the models are parameterised a little differently
    • BEAST has a wide range of 'demographic models' that can be used as prior distributions for the branch lengths
  • The input is also different
    • BEAST has an XML input format
    • MrBayes uses the Nexus format