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

Included with

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4 modules

Gain insight into a topic and learn the fundamentals.

Intermediate level

Recommended experience

2 weeks to complete

at 10 hours a week

Flexible schedule

Learn at your own pace

4 modules

Gain insight into a topic and learn the fundamentals.

Intermediate level

Recommended experience

2 weeks to complete

at 10 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

Tools you'll learn

Details to know

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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

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.

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

4 videos Total 72 minutes

The Inverse Transformation Method 12 minutes
The Rejection Method Part 1 19 minutes
The Rejection Method Part 2 11 minutes
R Tutorial 30 minutes

2 readings Total 60 minutes

Reading References 30 minutes
Reading References 30 minutes

3 assignments Total 90 minutes

Simulation 60 minutes
Random Variable Generation Techniques 15 minutes
R Tutorial 15 minutes

1 ungraded lab Total 60 minutes

Practice Lab: Simulating Random Variables in R 60 minutes

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

2 videos Total 71 minutes

Exponential Random Graphical Models 36 minutes
Stochastic Oriented Actor Models 35 minutes

2 readings Total 60 minutes

Reference data 20 minutes
Reading References 40 minutes

2 assignments Total 75 minutes

Exponential Random Graph Models 60 minutes
Advanced Network Modeling Techniques 15 minutes

1 ungraded lab Total 60 minutes

Practice Lab: Performing ERGM on Gray’s Anatomy Dataset 60 minutes

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

5 videos Total 65 minutes

Bayesian Network 12 minutes
Naive Bayes 13 minutes
Markov Blanket 10 minutes
Bayesian Inference 13 minutes
R Tutorial 17 minutes

2 readings Total 240 minutes

Reading References 120 minutes
Reading References 120 minutes

3 assignments Total 90 minutes

Probabilistic Graphical Models 60 minutes
Bayesian Network and Naive Bayes 15 minutes
Bayesian Analysis and R Fundamentals 15 minutes

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Instructors

Ian McCulloh

Johns Hopkins University

17 Courses 24,126 learners

Offered by

Johns Hopkins University

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