A basic question about phylogenetic inference with Bayes-MCMC
In short, the posterior is a probability value between 0 and 1 which is calculated from Bayes rule, which In phylogenetic usage is given as the product of the prior probability and the likelihood divided by a normalising constant. The posterior probability allows you to state the probability of some hypothesis given the data at hand (as opposed to the likelihood parlance, which is probability of data given hypothesis).

In a phylogenetic setting, the sampler works (roughly) like this: propose some value of a parameter (topology, branch lengths, etc.) and use the specified prior and calculated likelihood for that parameter value given the data to calculate the unnormalised posterior. Next, propose a new value, and repeat the same step. Compare the two values, and if the posterior probability of the second is greater, accept this parameter value and repeat the cycle again. In this way the Markov Chain moves through posterior space until it reaches a reasonably stable optimal approximation of the posterior distribution. Here, we want to get as many samples as possible to maximise the accuracy of our posterior. The maximum a posteriori (MAP) value of the parameter and its credible intervals, etc, will come from the distribution of these samples.