Chapter 3. Learning Parameters
Building a probabilistic graphical model requires in general three steps: defining the random variables, which are the nodes of the graph as well; defining the structure of the graph; and finally defining the numerical parameters of each local distribution. So far, the last step has been done manually and we have given numerical values to each local probability distribution by hand. In many cases, we have access to a wealth of data and we can find the numerical values of those parameters with a method called parameter learning. In other fields, it is also called parameter fitting or model calibration.
Parameter learning is one important topic in machine learning. In this chapter we will see how we can use a dataset and learn the parameters for a given graphical model. We will go from the simple but common use case, in which the data is fully observable, to a more complex case, in which the data is partially observed, and therefore needs more advanced...
