Introduction to Network Inference and Network Learning in R: Markov random Fields and Bayesian Networks
Unlock the Power of Network Inference Methods in the Real World, All in One Exciting Day!
In this introductory course, I will provide a brief overview of the two types of graphical models: undirected (Markov random fields) and directed (Bayesian networks). I will explain how these models can be applied in the real world and how to estimate (and interpret) the dependencies between variables of interest (in the form of a network) using various inference methods. We will also explore practical examples (using real data) and exercises (using R) to reinforce your learning.