Computational and Graphical Models in Probability
Master advanced probabilistic modeling techniques including simulation methods, exponential random graph models, and Bayesian networks using R programming.
Master advanced probabilistic modeling techniques including simulation methods, exponential random graph models, and Bayesian networks using R programming.
This course cannot be purchased separately - to access the complete learning experience, graded assignments, and earn certificates, you'll need to enroll in the full Statistical Methods for Computer Science Specialization program. You can audit this specific course for free to explore the content, which includes access to course materials and lectures. This allows you to learn at your own pace without any financial commitment.
Instructors:
English
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What you'll learn
Master techniques for simulating random variables using Inverse Transformation and Rejection Methods
Implement simulation algorithms in R programming for practical applications
Analyze complex networks using Exponential Random Graph Models
Interpret social structures and dependencies within relational data
Understand and apply probabilistic graphical models including Bayesian networks
Perform Bayesian inference and reasoning under uncertainty with real-world data
Skills you'll gain
This course includes:
3.43 Hours PreRecorded video
8 assignments
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FullTime access
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There are 4 modules in this course
This comprehensive course equips students with advanced computational and graphical techniques for probabilistic modeling and analysis of complex systems. The curriculum begins with simulation methods, teaching students to generate random variables using the Inverse Transformation and Rejection Methods with practical implementation in R programming. Students then progress to Exponential Random Graph Models (ERGMs), learning to analyze and interpret complex social and relational structures in networks. The final module covers Probabilistic Graphical Models, including Bayesian networks, Naive Bayes, and inference techniques, providing a framework for encoding probability distributions over large numbers of interacting random variables. Throughout the course, theoretical concepts are reinforced with hands-on R programming exercises and real-world applications, preparing students to solve complex problems in data analysis, machine learning, and statistical modeling across various domains.
Course Introduction
Module 1 · 14 Minutes to complete
Simulation
Module 2 · 4 Hours to complete
Exponential Random Graph Models
Module 3 · 4 Hours to complete
Probabilistic Graphical Models
Module 4 · 6 Hours to complete
Fee Structure
Instructors
Pioneering Social Network Analysis and AI at Johns Hopkins University
Dr. Ian McCulloh is an esteemed associate professor at Johns Hopkins University, holding joint appointments in the Bloomberg School of Public Health and the Whiting School of Engineering. His research focuses on social neuroscience, social network analysis, and the application of artificial intelligence to enhance understanding of online influence and strategic communication. With over 100 peer-reviewed publications and several influential books, including Social Network Analysis with Applications and ISIS in Iraq: Understanding the Social and Psychological Foundations of Terror, Dr. McCulloh has established himself as a leading voice in his field. He also founded the Brain Rise Foundation, a nonprofit dedicated to advancing neuroscience research for substance abuse recovery. Prior to his academic career, he had a distinguished military service, retiring as a Lieutenant Colonel after 20 years, during which he led innovative projects in data-driven social science research for countering extremism. Dr. McCulloh's multifaceted expertise and commitment to applying science for societal benefit make him a valuable asset to both academia and public health initiatives.
Instructor at Johns Hopkins University
Tony Johnson, affiliated with Johns Hopkins University, is an expert in teaching English courses, focusing on advanced probability and statistical methods. His teaching style emphasizes practical applications of statistical concepts to real-world problems
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