In the Bayesian approach, inference is based on the posterior density of the model parameters, P(η|Y), where η represents the vector of model parameters and Y the observed data. By Bayes’ rule, the density P(η|Y) is proportional to the product of the likelihood of the data given the model parameters P(Y|η) and the marginal density for η, P(η), that is,
