Here the prior distribution ( ) is a standard Gaussian  (0, ), and, by assuming i.i.d training datapoints, the likelihood term is (| ) = ∏ ∈ ( | ) where = ( , ) is a training instance.
the above integral is intractable, however an empirical estimate can be computed using  samples from the posterior, efficient generation of posterior samples is the key for applying the Bayesian treatment.