How can networks help us understand and predict social systems? How to find important individuals and communities? How to predict unobserved connections between genes? How to learn the dependencies between interrelated entities? How can we stop disease or information spreading in networks? In this course, we provide participants with the conceptual and practical skills necessary to use network science tools to answer social, economic and biological questions.
Course by tag
Introduction to Graphical Models (for Network Inference) in R: Markov random Fields and Bayesian Networks
Probabilistic graphical models (PGMs) represent the world in a simple way that humans can understand better.
They are graphical representations to understand the complex relationships between a set of random variables of interest (nodes). The edges that connect these nodes show the statistical dependencies between them.
PGMs have numerous applications in social and behavioral sciences, life sciences, economics, computer science, and many more fields.
In this introductory course, two types of graphical models are briefly introduced: undirected (Markov random fields) and directed (Bayesian networks). I will discuss how we can apply these models to estimate the dependencies between variables of interest in the form of a network. We will review excellent real-world examples and exercises (using R) to reinforce learning.
This course will open a new door for you and enable you to see the dependencies from a network point of view, which is much more comprehensive.