Computational and Graphical Models in Probability
Completed by Ananta Mahapatra
February 1, 2026
15 hours (approximately)
Ananta Mahapatra's account is verified. Coursera certifies their successful completion of Computational and Graphical Models in Probability
What you will 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 will gain
- Category: Statistical Methods
- Category: Network Analysis
- Category: Applied Machine Learning
- Category: Statistical Analysis
- Category: Data Visualization
- Category: Statistical Programming
- Category: Machine Learning
- Category: Statistical Modeling
- Category: Social Network Analysis
- Category: Sampling (Statistics)
- Category: Network Model
- Category: R (Software)
