So far in our discussion, we have always considered that we already know the network model as well as the parameters associated with the network. However, constructing these models requires a lot of domain knowledge. In most real-life problems, we usually have some recorded observations of the variables. So, in this chapter, we will learn to create models using the data we have.
To understand this problem, let's say that the domain is governed by some underlying distribution, 
. This distribution is induced by the network model, 
. Also, we are provided with a dataset, 
of M samples. As these data points are obtained from our observations of the actual model, we can say that these data points have been sampled from the distribution, . Also, we can assume that all the data samples have been independently sampled from the distribution, . Such data samples are known as independently and identically distributed (IID) samples...