Determining the hyperparameters that specify the variance of the prior and the variance of the output noise.
	• Ideally, when making a prediction, we would like to integrate out the hyperparameters, just like we integrate out the weights – But this is infeasible even when everything is Gaussian. • Empirical Bayes (also called the evidence approximation) means integrating out the parameters but maximizing over the hyperparameters. – Its more feasible and often works well. – It creates ideological disputes.