Approximate inference
	• What if we use an approximation to the posterior distribution over hidden configurations? – e.g. assume the posterior factorizes into a product of distributions for each separate hidden cause. • If we use the approximation for learning, there is no guarantee that learning will increase the probability that the model would generate the observed data. • But maybe we can find a different and sensible objective function that is guaranteed to improve at each update.