One method for sampling weight vectors
	• In standard backpropagation we keep moving the weights in the direction that decreases the cost – i.e. the direction that increases the log likelihood plus the log prior, summed over all training cases. • Suppose we add some Gaussian noise to the weight vector after each update. – So the weight vector never settles down. – It keeps wandering around, but it tends to prefer low cost regions of the weight space.