Summary
PGMs capture domain knowledge as relationships between variables and represent joint probabilities. They are used in a range of applications.
Probability maps an event to a real value between 0 and 1 and can be interpreted as a measure of the frequency of occurrence (frequentist view) or as a degree of belief in that occurrence (Bayesian view). Concepts of random variables, conditional probabilities, Bayes' theorem, chain rule, marginal and conditional independence and factors form the foundations to understanding PGMs. MAP and Marginal Map queries are ways to ask questions about the variables and relationships in the graph.
The structure of graphs and their properties such as paths, trails, cycles, sub-graphs, and cliques are vital to the understanding of Bayesian networks. Representation, Inference, and Learning form the core elements of networks that help us capture, extract, and make predictions using these methods. From the representation of graphs, we can reason about the flow...