The perfect solution to our learning task would be to find a model, , so that the probability distribution induced by it is the same as the underlying distribution of our data. However, this is never possible in real life because of computational costs and lack of data. So, as we can't find the exact underlying distribution, we try to optimize our learning task, depending on the goal of learning. To make it clearer, we can think of two different situations. Let's say in the first case, we want to learn the model to answer conditional queries over some specific variables, whereas in the second case, we want to answer multiple queries involving all the variables of the network. Therefore, in the first case, we would like to optimize our learning over variables, over which we want to answer queries...