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.
Computational and Graphical Models in Probability
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Computational and Graphical Models in Probability
This course is part of Statistical Methods for Computer Science Specialization
Instructors: Ian McCulloh
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Intermediate level
Recommended experience
15 hours to complete
3 weeks at 5 hours a week
Flexible schedule
Learn at your own pace
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
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Recently updated!
October 2024
Assessments
8 assignments
Taught in English
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This course is part of the Statistical Methods for Computer Science Specialization
When you enroll in this course, you'll also be enrolled in this Specialization.
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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
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