Mastering Probabilistic Graphical Models Using Python

Summary

In this chapter, we discussed special cases in graphical models that are widely used in the real world. We discussed the Naive Bayes model, which is a very simple model but is widely used in text classification and is known to give very good results. Then, we talked about DBNs, which are generally used in cases where we want to model some problem in which the values of the variables change with time. We discussed the Hidden Markov model, which is a very simple case of the DBN and is widely used in the field of speech recognition.