Probabilistic graphical models (PGMs) are a rich framework for encoding probability distributions over complex domains: joint (multivariate) distributions over large numbers of random variables that interact with each other. These representations sit at the intersection of statistics and computer science, relying on concepts from probability theory, graph algorithms, machine learning, and more. They are the basis for the state-of-the-art methods in a wide variety of applications, such as medical diagnosis, image understanding, speech recognition, natural language processing, and many, many more. They are also a foundational tool in formulating many machine learning problems.
This course is part of the Probabilistic Graphical Models Specialization
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Coursera Labs
Includes hands on learning projects.
Learn more about Coursera Labs Course 2 of 3 in the
Advanced Level
Approx. 38 hours to complete
English
Skills you will gain
- Inference
- Gibbs Sampling
- Markov Chain Monte Carlo (MCMC)
- Belief Propagation
Flexible deadlines
Reset deadlines in accordance to your schedule.
Shareable Certificate
Earn a Certificate upon completion
100% online
Start instantly and learn at your own schedule.
Coursera Labs
Includes hands on learning projects.
Learn more about Coursera Labs Course 2 of 3 in the
Advanced Level
Approx. 38 hours to complete
English
Offered by
Syllabus - What you will learn from this course
25 minutes to complete
Inference Overview
25 minutes to complete
2 videos (Total 25 min)
1 hour to complete
Variable Elimination
1 hour to complete
4 videos (Total 56 min)
18 hours to complete
Belief Propagation Algorithms
18 hours to complete
9 videos (Total 150 min)
2 hours to complete
MAP Algorithms
2 hours to complete
5 videos (Total 74 min)
15 hours to complete
Sampling Methods
15 hours to complete
5 videos (Total 100 min)
1 hour to complete
Inference in Temporal Models
1 hour to complete
1 video (Total 20 min)
Reviews
- 5 stars71.33%
- 4 stars21.12%
- 3 stars5.23%
- 2 stars1.04%
- 1 star1.25%
TOP REVIEWS FROM PROBABILISTIC GRAPHICAL MODELS 2: INFERENCE
by KDNov 4, 2018
Great introduction.
It would be great to have more examples included in the lectures and slides.
by RLFeb 23, 2021
Awesome class to gain better understanding of inference for graphical model
by JRDec 21, 2017
Great course! Expect to spend significant time reviewing the material.
by YPMay 28, 2017
I learned pretty much from this course. It answered my quandaries from the representation course, and as well deepened my understanding of PGM.
About the Probabilistic Graphical Models Specialization

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Learning Outcomes: By the end of this course, you will be able to take a given PGM and
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