So far, the interval estimators we have discussed are frequentists methods.  In this lesson, we discuss Bayesian credible sets - the correct name for Bayesian "confidence intervals."  In the frequentist interpretation, the parameter is a fixed quantity and the interval is random - so we say the (random) interval covers the (fixed) parameter.  In contrast, the Bayesian interval allows us to say that the value of the parameter is inside the realized interval with some probability.  This is because under the Bayesian paradigm, the parameter is modeled as a random variable with a probability distribution. All Bayesian claims of coverage are made with respect to the posterior distribution.