Bayesian regression analysis
In Bayesian inference the parameters θ are also considered as stochastic quantities with corresponding probability distribution π(·), called a priori distribution. In the case of continuous parameters the a priori distribution is determined by a probability density π(·) on the parameter space
Then π(·) is a probability density on the parameter space, called a priori density.
The stochastic model for the dependent variable y is Yx ~ fx(·|θ).