Learning Probabilistic Graphical Models in R

Summary

In the second chapter, we introduced the fundamentals of inference and we saw the most important algorithms for computing posterior distribution: variable elimination and the junction tree algorithm. We learned how to build a graphical model by considering causality, temporal relationships, and by identifying patterns between variables. We saw a fundamental feature of probabilistic graphical models, which is the combination of graphs to build more complex models. And we learned how to perform inference with a junction tree algorithm in R and saw that the same junction tree can be used for any type of query, on both marginal and joint distribution. In the last section we saw several real-life examples of PGM that can be used in many applications ...