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.
Give your career the gift of Coursera Plus with $160 off, billed annually. Save today.
Computational and Graphical Models in Probability
This course is part of Statistical Methods for Computer Science Specialization
Instructors: Ian McCulloh
Included with
Recommended experience
What you'll learn
Master techniques for simulating random variables, including the Inverse Transformation and Rejection Methods using R programming.
Analyze complex networks using Exponential Random Graph Models to model and interpret social structures and their dependencies.
Understand and apply probabilistic graphical models, including Bayesian networks, to reason about uncertainty and infer relationships in data.
Skills you'll gain
Details to know
Add to your LinkedIn profile
October 2024
8 assignments
See how employees at top companies are mastering in-demand skills
Build your subject-matter expertise
- Learn new concepts from industry experts
- Gain a foundational understanding of a subject or tool
- Develop job-relevant skills with hands-on projects
- Earn a shareable career certificate
Earn a career certificate
Add this credential to your LinkedIn profile, resume, or CV
Share it on social media and in your performance review
There are 4 modules in this course
This course covers advanced techniques in network and probabilistic modeling, including simulation methods, exponential random graph models, and probabilistic graphical models. You will gain practical skills in analyzing complex systems and relational data.
What's included
2 readings1 plugin
This module develops student proficiency in simulating random variables for arbitrary density functions. Students will be introduced to the Inverse Transformation Method and the Rejection Method.
What's included
4 videos2 readings3 assignments1 ungraded lab
Exponential Random Graph Models introduce the use of exponential random graph models (ERGMs) for network analysis. You will learn how to model and interpret complex social and relational structures.
What's included
2 videos2 readings2 assignments1 ungraded lab
This module introduces a framework for encoding probability distributions over complex joint domains over large numbers of random variables that interact with one another. Students will become familiar with probabilistic graph model applications to many machine learning problems.
What's included
5 videos2 readings3 assignments
Offered by
Recommended if you're interested in Probability and Statistics
University of California, Santa Cruz
Duke University
Duke University
Why people choose Coursera for their career
New to Probability and Statistics? Start here.
Open new doors with Coursera Plus
Unlimited access to 7,000+ world-class courses, hands-on projects, and job-ready certificate programs - all included in your subscription
Advance your career with an online degree
Earn a degree from world-class universities - 100% online
Join over 3,400 global companies that choose Coursera for Business
Upskill your employees to excel in the digital economy
Frequently asked questions
Access to lectures and assignments depends on your type of enrollment. If you take a course in audit mode, you will be able to see most course materials for free. To access graded assignments and to earn a Certificate, you will need to purchase the Certificate experience, during or after your audit. If you don't see the audit option:
The course may not offer an audit option. You can try a Free Trial instead, or apply for Financial Aid.
The course may offer 'Full Course, No Certificate' instead. This option lets you see all course materials, submit required assessments, and get a final grade. This also means that you will not be able to purchase a Certificate experience.
When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile. If you only want to read and view the course content, you can audit the course for free.
If you subscribed, you get a 7-day free trial during which you can cancel at no penalty. After that, we don’t give refunds, but you can cancel your subscription at any time. See our full refund policy.