Chapter 1, Probability, covers the concepts of probability required to understand the
graphical models.
Chapter 2, Directed Graphical Models, provides information about Bayesian
networks, their properties related to independence, conditional independence,
and D-separation. This chapter uses code snippets to load a Bayes network and
understand its independence properties.
Chapter 3, Undirected Graphical Models, covers the properties of Markov networks,
how they are different from Bayesian networks, and their independence properties.
Chapter 4, Structure Learning, covers multiple approaches to infer the structure of the
Bayesian network using a dataset. We also learn the computational complexity of
structure learning and use code snippets in this chapter to learn the structures given
in the sampled datasets.