 Just as with likelihood methods, Bayesian methods require a model of evolution before any inferences are possible. The fundamental difference is that likelihood inference treats parameters of the model as nuisance parameters which have a fixed but unknown value. In the frequentist method of maximum likelihood, these parameters are set to their best fitting value. Bayesian inference treats parameters as random variables with unknown distributions and probabilities are used as a measure of uncertainty. The probability of a tree is a conditional probability \(P(\text{Tree} | \text{Data})\) under the assumption of a particular model. When we incorporate the model parameters \(\theta\), by Bayes’ theorem, we have: