Learning Probabilistic Graphical Models in R

Learning by inference

In the introduction to this chapter, we saw that learning can be done in a frequentist way by counting data. In most cases, it will be sufficient, but it is also a narrow view of the notion of learning. More generally speaking, learning is the problem of integrating data into the domain knowledge in order to create a new model or improve an existing model. Therefore, learning can be seen as an inference problem, where one updates an existing model toward a better model.

Let's consider a simple problem: modeling the results of tossing a coin. We want to test if the coin is fair or not. Let's call θ the probability that the coin lands on its head. A fair throw would have a probability of 0.5. By tossing the coin several times ...