Approximating full Bayesian learning in a neural network
			• If the neural net only has a few parameters we could put a grid over the parameter space and evaluate p( W | D ) at each grid-point. – This is expensive, but it does not involve any gradient descent and there are no local optimum issues. • After evaluating each grid point we use all of them to make predictions on test data – This is also expensive, but it works much better than ML learning when the posterior is vague or multimodal (this happens when data is scarce).