About this Course
The course "Computational and Graphical Models in Probability" equips learners with essential skills to analyze complex systems through simulation techniques and network analysis. By exploring advanced concepts such as Exponential Random Graph Models and Probabilistic Graphical Models, students will learn to model and interpret intricate social structures and dependencies within data. What sets this course apart is its emphasis on practical applications using the R programming language, empowering students to simulate random variables effectively and construct sophisticated models for real-world scenarios. Through hands-on projects and exercises, learners will not only deepen their theoretical understanding but also gain valuable experience in solving applied problems across various domains. Upon completion, you will be well-prepared to tackle challenges in data analysis, machine learning, and statistical modeling, making you a valuable asset in any data-driven field. Whether you're looking to enhance your expertise or start a new career, this course offers a unique blend of theory and practical skills that will enable you to excel in today’s data-centric world
What You'll Learn
- Master random variable simulation techniques with R
- Analyze complex networks using Exponential Random Graph Models
- Apply probabilistic graphical models to infer relationships
- Understand social data structures and dependencies
- Solve applied problems using advanced models
Prerequisites
- Basic familiarity with probability concepts and terminology
- Willingness to engage in applied exercises and case studies
Instructors
Ian McCulloh
Tony Johnson