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.
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.
🌟 Are you ready to uncover the secrets of graphical models, revolutionize your understanding of real-world data, and unravel the hidden relationships between variables across diverse disciplines?
Graphical models represent the real-world in a very simple way. 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. Graphical Models have numerous applications in social and behavioral sciences, life sciences, economics, computer science, and many other fields.
🔍 What to Expect:
• Uncover the magic behind graphical models by demystifying edges and nodes with real-life examples.
• Master the concept of (conditional) dependency and independency, shedding light on the significance of edges through real-world applications.
• Dive deep into practical terminologies such as paths, blocked paths, the flow of information, d-separation, Markov blanket, and more, simplifying the intricate web of variable relationships and empowering you to interpret them.
• Explore methods for Network Inference, enabling you to estimate the presence or absence of edges between variables of interest and to determine network structures, providing a comprehensive understanding of relationships between all elements.
• To this end, immerse yourself in the world of Markov Random Fields for undirected relationships and Bayesian Networks for directed relationships—two well-known powerful tools that help you understand network inference and learning, allowing you to explore hidden relationships between variables in the real world.
• Learn how to estimate undirected network structures from both continuous and discrete data using the Graphical Lasso approach and the Ising model, with a primary focus on the Graphical Lasso.
• Gain expertise in comprehending Bayesian Networks and their application to continuous, discrete, and mixed variables, with a primary focus on the first two.
• Learn how to estimate directed network structures from both continuous and discrete data using both Constraint-based and network score approaches.
📚 Hands-On Experience:
After each lecture, you will engage in practical exercises using the R programming language. Immerse yourself in hands-on activities, putting your newfound knowledge to the test in real-world scenarios.
During the lunch breaks and the in-class practicals, participants will have the opportunity to discuss how to apply the methods to their own data
Don't miss this opportunity to elevate your expertise in Network inference, gaining a competitive edge in your field. Secure your spot now and embark on a transformative learning journey!
As a prerequisite, participants should be familiar with basic statistics, probability, and basic R programming.
Please note that R and RStudio should be installed on your own laptop in advance. The software is available online.
R (Windows): https://cran.r-project.org/bin/windows/base/
R (MacOS) : https://cran.r-project.org/bin/macosx/
In case, you have trouble installing, watch the following;
Tutorial video for introduction to R programing:
Mahdi Shafiee Kamalabad
Researchers, students, engineers, and analysts.
For an overview of all our Winter school courses offered by the Department of Methodology and Statistics please click here.
Aim of the course
The aim of this course is to provide fundamental knowledge of graphical models for network inference for those people such as medical scientists, psychologists, biologists, economists, and other researchers who wish to learn the network structure for discovering the relationships between variables of interest.
By the end of this course, students will be able to:
- Understand Graphical Models: Gain a sufficient understanding of Graphical Models (Bayesian Networks and Markov Random Fields) and their real-world applications across various fields.
- Interpret Network Structures: Learn how to interpret network structures, including the meaning of nodes, edges, (in)dependency, and (blocked) paths. Discover how these relate to the dependencies between variables and their impact on the presence or absence of edges.
- Network Structure Estimation: Explore various network inference methods and acquire the skills to estimate undirected and directed network structures. These structures illustrate the dependencies between variables of interest, and you will use the R programming language for this purpose.
- When to Use Which Method: Compare Markov Random Fields and Bayesian Networks and gain insights into when to choose each method.
This one-day introductory course is dedicated to only network inference, where nodes represent variables, facilitating the discovery of relationships between variables in the form of a network. It serves as a part of the comprehensive 'Network Science' course offered at Utrecht University's summer school, covering a wide range of networks, network analysis and network modeling.
One day (10.00 – 17.00). Lectures and computer lab exercises will be alternated during this informal one-day workshop.
You will receive a certificate upon course completion. Please be aware that this course does not include graded activities, and therefore, we cannot provide a transcript of grades.
- Tuition fee for PhD students from the Faculty of Social and Behavioural Sciences from Utrecht University will be funded by the Graduate School of Social and Behavioural Sciences.
- The tuition fee for staff of the Faculty of Social and Behavioural Sciences from Utrecht University will be funded by FSBS.
Utrecht Summer School does not offer scholarships for this course.
Irma Reyersen | E: email@example.com