In each iteration of an MCMC algorithm, a sample is taken from each conditional posterior distribution for each subset of the parameter space, given the current values of the other parameters. After a number of so-called ‘‘burn-in’’ iterations, necessary for a chain to achieve stationarity (i.e., approaching the target distribution: the joint posterior distribution) sufficiently closely, the subsequent draws can be regarded as sampled from the joint posterior distribution.
