The next step in this tutorial is to create a Chain class that carries out a Markov chain Monte Carlo (MCMC) simulation for the purpose of sampling from a Bayesian posterior distribution. Our initial effort will sample trees and edge lengths but keep substitution model parameters (e.g. nucleotide state frequencies, GTR exchangeabilities, and discrete gamma rate heterogeneity shape parameter) fixed at their initial values. Later we will add the ability to sample these as well.