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
About this Course
Skills you will gain
- Gibbs Sampling
- Markov Chain Monte Carlo (MCMC)
- Belief Propagation
Syllabus - What you will learn from this course
Belief Propagation Algorithms
Inference in Temporal Models
- 5 stars71.33%
- 4 stars21.12%
- 3 stars5.23%
- 2 stars1.04%
- 1 star1.25%
TOP REVIEWS FROM PROBABILISTIC GRAPHICAL MODELS 2: INFERENCE
It would be great to have more examples included in the lectures and slides.
Great course! Expect to spend significant time reviewing the material.
I would have like to complete the honors assignments, unfortunately, I'm not fluent in Matlab. Otherwise, great course!
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
Frequently Asked Questions
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Is financial aid available?
Learning Outcomes: By the end of this course, you will be able to take a given PGM and
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