Learning Probabilistic Graphical Models in R

Summary

In this first chapter we learned the base concepts of probabilities

We saw how and why they are used to represent uncertainty about data and knowledge, while also introducing the Bayes formula. This is the most important formula to compute posterior probabilities—that is, to update our beliefs and knowledge about a fact when new data is available

We saw what a joint probability distribution is and learnt that they can quickly become too complex and intractable to deal with. We learned the basics of probabilistic graphical models as a generic framework to perform tractable, efficient, and easy modeling with probabilistic models. Finally, we introduced the different types of probabilistic graphical model and learned how to use R packages to ...