Johns Hopkins University
Computational and Graphical Models in Probability
Johns Hopkins University

Computational and Graphical Models in Probability

Ian McCulloh

Instructor: Ian McCulloh

Gain insight into a topic and learn the fundamentals.
Intermediate level

Recommended experience

15 hours to complete
3 weeks at 5 hours a week
Flexible schedule
Learn at your own pace
Gain insight into a topic and learn the fundamentals.
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.

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

Instructor

Ian McCulloh
Johns Hopkins University
8 Courses186 learners

Offered by

Recommended if you're interested in Probability and Statistics

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